U.S. patent application number 12/662559 was filed with the patent office on 2010-10-14 for method of evaluating igt, igt-evaluating apparatus, igt-evaluating method, igt--evaluating system, igt-evaluating program, recording medium, and method of searching for prophylactic/ameliorating substance for igt.
This patent application is currently assigned to Ajinomoto Co., Inc.. Invention is credited to Toshihiko Ando, Mitsuo Takahashi, Minoru Yamakado.
Application Number | 20100261282 12/662559 |
Document ID | / |
Family ID | 40579455 |
Filed Date | 2010-10-14 |
United States Patent
Application |
20100261282 |
Kind Code |
A1 |
Takahashi; Mitsuo ; et
al. |
October 14, 2010 |
Method of evaluating IGT, IGT-evaluating apparatus, IGT-evaluating
method, IGT--evaluating system, IGT-evaluating program, recording
medium, and method of searching for prophylactic/ameliorating
substance for IGT
Abstract
According to the method of evaluating IGT of the present
invention, amino acid concentration data on concentration values of
amino acids in blood collected from a subject to be evaluated is
measured, and an impaired glucose tolerance state in the subject is
evaluated based on the measured amino acid concentration data of
the subject.
Inventors: |
Takahashi; Mitsuo;
(Kanagawa, JP) ; Ando; Toshihiko; (Kanagawa,
JP) ; Yamakado; Minoru; (Tokyo, JP) |
Correspondence
Address: |
FOLEY AND LARDNER LLP;SUITE 500
3000 K STREET NW
WASHINGTON
DC
20007
US
|
Assignee: |
Ajinomoto Co., Inc.
|
Family ID: |
40579455 |
Appl. No.: |
12/662559 |
Filed: |
April 22, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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PCT/JP2008/068981 |
Oct 20, 2008 |
|
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12662559 |
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Current U.S.
Class: |
436/86 ;
702/19 |
Current CPC
Class: |
G01N 33/6806 20130101;
G01N 33/6893 20130101; G16B 40/00 20190201; A61P 3/10 20180101;
G01N 2800/042 20130101; A61B 5/14546 20130101 |
Class at
Publication: |
436/86 ;
702/19 |
International
Class: |
G01N 33/68 20060101
G01N033/68; G06F 19/00 20060101 G06F019/00 |
Foreign Application Data
Date |
Code |
Application Number |
Oct 25, 2007 |
JP |
2007-277793 |
Claims
1. A method of evaluating IGT, comprising: a measuring step of
measuring amino acid concentration data on a concentration value of
an amino acid in blood collected from a subject to be evaluated;
and a concentration value criterion evaluating step of evaluating
an impaired glucose tolerance state in the subject based on the
amino acid concentration data of the subject measured at the
measuring step.
2. The method of evaluating IGT according to claim 1, wherein at
the concentration value criterion evaluating step, the impaired
glucose tolerance state in the subject is evaluated based on the
concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the amino acid concentration data of
the subject measured at the measuring step.
3. The method of evaluating IGT according to claim 2, wherein the
concentration value criterion evaluating step further includes a
concentration value criterion discriminating step of discriminating
between an impaired glucose tolerance and a normal glucose
tolerance in the subject based on the concentration value of at
least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in
the amino acid concentration data of the subject measured at the
measuring step.
4. The method of evaluating IGT according to claim 3, wherein at
the concentration value criterion discriminating step, the
discrimination between the impaired glucose tolerance and the
normal glucose tolerance in the subject is conducted based on the
concentration values of at least two of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the amino acid concentration data of
the subject measured at the measuring step.
5. The method of evaluating IGT according to claim 1, wherein the
concentration value criterion evaluating step further includes: a
discriminant value calculating step of calculating a discriminant
value that is a value of a multivariate discriminant with a
concentration of the amino acid as an explanatory variable, based
on both the amino acid concentration data of the subject measured
at the measuring step and the previously established multivariate
discriminant; and a discriminant value criterion evaluating step of
evaluating the impaired glucose tolerance state in the subject
based on the discriminant value calculated at the discriminant
value calculating step.
6. The method of evaluating IGT according to claim 5, wherein the
multivariate discriminant contains at least one of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variable, and wherein
at the discriminant value calculating step, the discriminant value
is calculated based on both the concentration value of at least one
of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the amino
acid concentration data of the subject measured at the measuring
step and the multivariate discriminant.
7. The method of evaluating IGT according to claim 6, wherein the
discriminant value criterion evaluating step further includes a
discriminant value criterion discriminating step of discriminating
between an impaired glucose tolerance and a normal glucose
tolerance in the subject based on the discriminant value calculated
at the discriminant value calculating step.
8. The method of evaluating IGT according to claim 7, wherein the
multivariate discriminant contains at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variables, and
wherein at the discriminant value calculating step, the
discriminant value is calculated based on both the concentration
values of at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the subject
measured at the measuring step and the multivariate
discriminant.
9. The method of evaluating IGT according to claim 8, wherein the
multivariate discriminant is expressed by one fractional expression
or the sum of a plurality of the fractional expressions, and
contains at least one of Glu, Ile, Val, Leu, Phe and Asp as the
explanatory variable in the numerator in the fractional expression
constituting the multivariate discriminant, and at least one of Gly
and Ser as the explanatory variable in the denominator in the
fractional expression constituting the multivariate discriminant,
or contains at least one of Gly and Ser as the explanatory variable
in the numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the denominator in the
fractional expression constituting the multivariate
discriminant.
10. The method of evaluating IGT according to claim 9, wherein the
multivariate discriminant is formula 1: Glu/(His+Cit)+(Phe+Tyr)/Gly
(formula 1).
11. The method of evaluating IGT according to claim 8, wherein the
multivariate discriminant is any one of a logistic regression
equation, a linear discriminant, a multiple regression equation, a
discriminant prepared by a support vector machine, a discriminant
prepared by a Mahalanobis' generalized distance method, a
discriminant prepared by canonical discriminant analysis, and a
discriminant prepared by a decision tree.
12. The method of evaluating IGT according to claim 11, wherein the
multivariate discriminant is the logistic regression equation
containing at least Glu and Gly as the explanatory variables.
13. An IGT-evaluating apparatus comprising a control unit and a
memory unit to evaluate an impaired glucose tolerance state in a
subject to be evaluated, wherein the control unit includes: a
discriminant value-calculating unit that calculates a discriminant
value that is a value of a multivariate discriminant with a
concentration of an amino acid as an explanatory variable, based on
both previously obtained amino acid concentration data of the
subject on a concentration value of the amino acid and the
multivariate discriminant stored in the memory unit; and a
discriminant value criterion-evaluating unit that evaluates the
impaired glucose tolerance state in the subject based on the
discriminant value calculated by the discriminant value-calculating
unit.
14. The IGT-evaluating apparatus according to claim 13, wherein the
multivariate discriminant contains at least one of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variable, and wherein
the discriminant value-calculating unit calculates the discriminant
value based on both the concentration value of at least one of Glu,
Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the previously
obtained amino acid concentration data of the subject and the
multivariate discriminant.
15. The IGT-evaluating apparatus according to claim 14, wherein the
discriminant value criterion-evaluating unit further includes a
discriminant value criterion-discriminating unit that discriminates
between an impaired glucose tolerance and a normal glucose
tolerance in the subject based on the discriminant value calculated
by the discriminant value-calculating unit.
16. The IGT-evaluating apparatus according to claim 15, wherein the
multivariate discriminant contains at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variables, and
wherein the discriminant value-calculating unit calculates the
discriminant value based on both the concentration values of at
least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in
the previously obtained amino acid concentration data of the
subject and the multivariate discriminant.
17. The IGT-evaluating apparatus according to claim 16, wherein the
multivariate discriminant is expressed by one fractional expression
or the sum of a plurality of the fractional expressions, and
contains at least one of Glu, Ile, Val, Leu, Phe and Asp as the
explanatory variable in the numerator in the fractional expression
constituting the multivariate discriminant, and at least one of Gly
and Ser as the explanatory variable in the denominator in the
fractional expression constituting the multivariate discriminant,
or contains at least one of Gly and Ser as the explanatory variable
in the numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the denominator in the
fractional expression constituting the multivariate
discriminant.
18. The IGT-evaluating apparatus according to claim 17, wherein the
multivariate discriminant is formula 1: Glu/(His+Cit)+(Phe+Tyr)/Gly
(formula 1)
19. The IGT-evaluating apparatus according to claim 16, wherein the
multivariate discriminant is any one of a logistic regression
equation, a linear discriminant, a multiple regression equation, a
discriminant prepared by a support vector machine, a discriminant
prepared by a Mahalanobis' generalized distance method, a
discriminant prepared by canonical discriminant analysis, and a
discriminant prepared by a decision tree.
20. The IGT-evaluating apparatus according to claim 19, wherein the
multivariate discriminant is the logistic regression equation
containing at least Glu and Gly as the explanatory variables.
21. The IGT-evaluating apparatus according to claim 13, wherein the
control unit further includes a multivariate discriminant-preparing
unit that prepares the multivariate discriminant stored in the
memory unit, based on impaired glucose tolerance state information
stored in the memory unit containing the amino acid concentration
data and impaired glucose tolerance state index data on an index
for indicating the impaired glucose tolerance state, wherein the
multivariate discriminant-preparing unit further includes: a
candidate multivariate discriminant-preparing unit that prepares a
candidate multivariate discriminant that is a candidate of the
multivariate discriminant, based on a predetermined
discriminant-preparing method from the impaired glucose tolerance
state information; a candidate multivariate discriminant-verifying
unit that verifies the candidate multivariate discriminant prepared
by the candidate multivariate discriminant-preparing unit, based on
a predetermined verifying method; and an explanatory
variable-selecting unit that selects the explanatory variable of
the candidate multivariate discriminant based on a predetermined
explanatory variable-selecting method from a verification result
obtained by the candidate multivariate discriminant-verifying unit,
thereby selecting a combination of the amino acid concentration
data contained in the impaired glucose tolerance state information
used in preparing the candidate multivariate discriminant, and
wherein the multivariate discriminant-preparing unit prepares the
multivariate discriminant by selecting the candidate multivariate
discriminant used as the multivariate discriminant, from a
plurality of the candidate multivariate discriminants, based on the
verification results accumulated by repeatedly executing the
candidate multivariate discriminant-preparing unit, the candidate
multivariate discriminant-verifying unit, and the explanatory
variable-selecting unit.
22. An IGT-evaluating method of evaluating an impaired glucose
tolerance state in a subject to be evaluated, the method is carried
out with an information processing apparatus including a control
unit and a memory unit, the method comprising: (i) a discriminant
value calculating step of calculating a discriminant value that is
a value of a multivariate discriminant with a concentration of an
amino acid as an explanatory variable, based on both previously
obtained amino acid concentration data of the subject on a
concentration value of the amino acid and the multivariate
discriminant stored in the memory unit; and (ii) a discriminant
value criterion evaluating step of evaluating the impaired glucose
tolerance state in the subject based on the discriminant value
calculated at the discriminant value calculating step, wherein the
steps (i) and (ii) are executed by the control unit.
23. The IGT-evaluating method according to claim 22, wherein the
multivariate discriminant contains at least one of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variable, and wherein
at the discriminant value calculating step, the discriminant value
is calculated based on both the concentration value of at least one
of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
previously obtained amino acid concentration data of the subject
and the multivariate discriminant.
24. The IGT-evaluating method according to claim 23, wherein the
discriminant value criterion evaluating step further includes a
discriminant value criterion discriminating step of discriminating
between an impaired glucose tolerance and a normal glucose
tolerance in the subject based on the discriminant value calculated
at the discriminant value calculating step.
25. The IGT-evaluating method according to claim 24, wherein the
multivariate discriminant contains at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variables, and
wherein at the discriminant value calculating step, the
discriminant value is calculated based on both the concentration
values of at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the previously obtained amino acid concentration data
of the subject and the multivariate discriminant.
26. The IGT-evaluating method according to claim 25, wherein the
multivariate discriminant is expressed by one fractional expression
or the sum of a plurality of the fractional expressions, and
contains at least one of Glu, Ile, Val, Leu, Phe and Asp as the
explanatory variable in the numerator in the fractional expression
constituting the multivariate discriminant, and at least one of Gly
and Ser as the explanatory variable in the denominator in the
fractional expression constituting the multivariate discriminant,
or contains at least one of Gly and Ser as the explanatory variable
in the numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the denominator in the
fractional expression constituting the multivariate
discriminant.
27. The IGT-evaluating method according to claim 26, wherein the
multivariate discriminant is formula 1: Glu/(His+Cit)+(Phe+Tyr)/Gly
(formula 1).
28. The IGT-evaluating method according to claim 25, wherein the
multivariate discriminant is any one of a logistic regression
equation, a linear discriminant, a multiple regression equation, a
discriminant prepared by a support vector machine, a discriminant
prepared by a Mahalanobis' generalized distance method, a
discriminant prepared by canonical discriminant analysis, and a
discriminant prepared by a decision tree.
29. The IGT-evaluating method according to claim 28, wherein the
multivariate discriminant is the logistic regression equation
containing at least Glu and Gly as the explanatory variables.
30. The IGT-evaluating method according to claim 22, wherein the
method further includes a multivariate discriminant preparing step
of preparing the multivariate discriminant stored in the memory
unit, based on impaired glucose tolerance state information stored
in the memory unit containing the amino acid concentration data and
impaired glucose tolerance state index data on an index for
indicating the impaired glucose tolerance state, that is executed
by the control unit, wherein the multivariate discriminant
preparing step further includes: a candidate multivariate
discriminant preparing step of preparing a candidate multivariate
discriminant that is a candidate of the multivariate discriminant,
based on a predetermined discriminant-preparing method from the
impaired glucose tolerance state information; a candidate
multivariate discriminant verifying step of verifying the candidate
multivariate discriminant prepared at the candidate multivariate
discriminant preparing step, based on a predetermined verifying
method; and an explanatory variable selecting step of selecting the
explanatory variable of the candidate multivariate discriminant
based on a predetermined explanatory variable-selecting method from
a verification result obtained at the candidate multivariate
discriminant verifying step, thereby selecting a combination of the
amino acid concentration data contained in the impaired glucose
tolerance state information used in preparing the candidate
multivariate discriminant, and wherein at the multivariate
discriminant preparing step, the multivariate discriminant is
prepared by selecting the candidate multivariate discriminant used
as the multivariate discriminant from a plurality of the candidate
multivariate discriminants, based on the verification results
accumulated by repeatedly executing the candidate multivariate
discriminant preparing step, the candidate multivariate
discriminant verifying step, and the explanatory variable selecting
step.
31. An IGT-evaluating system comprising an IGT-evaluating apparatus
including a control unit and a memory unit to evaluate an impaired
glucose tolerance state in a subject to be evaluated and an
information communication terminal apparatus that provides amino
acid concentration data of the subject on a concentration value of
an amino acid connected to each other communicatively via a
network, wherein the information communication terminal apparatus
includes: an amino acid concentration data-sending unit that
transmits the amino acid concentration data of the subject to the
IGT-evaluating apparatus; and an evaluation result-receiving unit
that receives an evaluation result on the impaired glucose
tolerance state of the subject transmitted from the IGT-evaluating
apparatus, wherein the control unit of the IGT-evaluating apparatus
includes: an amino acid concentration data-receiving unit that
receives the amino acid concentration data of the subject
transmitted from the information communication terminal apparatus;
a discriminant value-calculating unit that calculates a
discriminant value that is a value of a multivariate discriminant
with a concentration of the amino acid as an explanatory variable,
based on both the amino acid concentration data of the subject
received by the amino acid concentration data-receiving unit and
the multivariate discriminant stored in the memory unit; a
discriminant value criterion-evaluating unit that evaluates the
impaired glucose tolerance state in the subject based on the
discriminant value calculated by the discriminant value-calculating
unit; and an evaluation result-sending unit that transmits the
evaluation result of the subject obtained by the discriminant value
criterion-evaluating unit to the information communication terminal
apparatus.
32. An IGT-evaluating program product that makes an information
processing apparatus including a control unit and a memory unit
execute a method of evaluating an impaired glucose tolerance state
in a subject to be evaluated, the method comprising: (i) a
discriminant value calculating step of calculating a discriminant
value that is a value of a multivariate discriminant with a
concentration of an amino acid as an explanatory variable, based on
both previously obtained amino acid concentration data of the
subject on a concentration value of the amino acid and the
multivariate discriminant stored in the memory unit; and (ii) a
discriminant value criterion evaluating step of evaluating the
impaired glucose tolerance state in the subject based on the
discriminant value calculated at the discriminant value calculating
step, wherein the steps (i) and (ii) are executed by the control
unit.
33. A computer-readable recording medium, comprising the
IGT-evaluating program product according to claim 32 recorded
thereon.
34. A method of searching for prophylactic/ameliorating substance
for IGT, comprising: a measuring step of measuring amino acid
concentration data on a concentration value of an amino acid in
blood collected from a subject to be evaluated to which a desired
substance group consisting of one or more substances that prevent
an impaired glucose tolerance or ameliorate an impaired glucose
tolerance state has been administered; a concentration value
criterion evaluating step of evaluating an impaired glucose
tolerance state in the subject based on the amino acid
concentration data measured at the measuring step; and a judging
step of judging whether or not the desired substance group prevents
the impaired glucose tolerance or ameliorates the impaired glucose
tolerance state, based on an evaluation result obtained at the
concentration value criterion evaluating step.
Description
[0001] This application is a Continuation of PCT/JP2008/068981,
filed Oct. 20, 2008, which claims priority from Japanese patent
application JP 2007-277793 filed Oct. 25, 2007. The contents of
each of the aforementioned application are incorporated herein by
reference in their entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to a method of evaluating IGT
(impaired glucose tolerance), an IGT-evaluating apparatus, an
IGT-evaluating method, an IGT-evaluating system, an IGT-evaluating
program, and a recording medium, which utilize concentrations of
amino acids in blood (plasma).
[0004] The present invention also relates to a method of searching
for prophylactic/ameliorating substance for IGT, wherein a
substance for preventing an IGT or ameliorating an IGT state is
searched.
[0005] 2. Description of the Related Art
[0006] According to a survey on the actuality of diabetes conducted
by Ministry of Health, Labour and Welfare, Japan, in 2002 (refer to
"Homepage of Ministry of Health, Labour and Welfare, Japan, a
survey on the actuality of diabetes mellitus, 2002"), approximately
7.4 million people were "those who were strongly suspected of
having diabetes (those whose HbA1c (hemoglobin A1c) were equal to
or more than 6.1% or those who were receiving treatment for
diabetes as of the time of survey)" and approximately 8.8 million
people were "those in whom the possibility of diabetes could not be
ruled out (those whose HbA1c were equal to or more than 5.6% and
less than 6.1% and who were not receiving treatment for diabetes as
of the time of survey)", and the number of these people was summed
up to approximately 16.2 million nationwide. That is, one in every
six adults was estimated to suffer from diabetes or prediabetes.
This survey elucidated the actuality of diabetes, which has come to
be called "the national disease of the 21st century", and posed a
significant problem in terms of health and medical care of the
population.
[0007] In impaired glucose tolerance, which is a prediabetic, risks
of developing diabetic complications associated with future
progression to diabetic and cardiovascular disorders due to
arteriosclerosis are high. Recently, the number of people who have
symptoms of metabolic syndrome, which is manifested as a
combination of a plurality of obesity, hyperglycemia based on
insulin resistance, hypertension, and hyperlipidemia, is increasing
due to lifestyles under the background of high-fat diet and lack of
exercise. Currently, these symptoms are becoming a focus of
attention as an urgent issue in health insurance because they are
perceived to increase the number of people who develop
arteriosclerosis over time and eventually result in cardiovascular
disorders such as myocardial infarction and cerebrovascular
disorders such as cerebral infarction. Both diabetes and metabolic
syndrome are attributable to lifestyles, and are strongly
correlated with each other.
[0008] According to a classification provided by American Diabetes
Association (ADA) in 1997 (refer to "The Expert Committee on the
Diagnosis and Classification of Diabetes Mellitus, Diabetes Care,
20, 1183 (1997)."), diabetes and prediabetes that is prone to
develop into diabetes are assessed based on the following
assessment category using Fasting Plasma Glucose (FPG) and Oral
Glucose Tolerance Test (OGTT). Whether a postprandial blood glucose
level is high or low is assessed based on blood glucose levels
measured over time subsequent to ingestion of solution containing
75 g of glucose in OGTT. Because particularly a 2-hour OGTT value
is used in the assessment, 2-hour OGTT values are abbreviated as
OGTT below.
Assessment Category
[0009] When FPG is equal to or more than 126 mg/dl or OGTT is equal
to or more than 200 mg/dl, a state is assessed as diabetes.
[0010] When FPG is equal to or more than 110 mg/dl and less than
126 mg/dl and OGTT is less than 140 mg/dl, a state is assessed as
Impaired Fasting Glucose: IFG.
[0011] When FPG is less than 110 mg/dl and OGTT is equal to or more
than 140 mg/dl and less than 200 mg/dl, a state is assessed as
Impaired Glucose Tolerance: IGT.
[0012] When FPG is equal to or more than 110 mg/dl and less than
126 mg/dl and OGTT is equal to or more than 140 mg/dl and less than
200 mg/dl, a state is assessed as IFG/IGT.
[0013] When FPG is less than 110 mg/dl and OGTT is less than 140
mg/dl, a state is assessed as Normal Glucose Tolerance (NGT).
[0014] According to a categorization provided by Japan Diabetes
Society in 1999 (refer to "KUZUYA, Takeshi et al., Commission
report on classification and diagnostic criteria for diabetes
mellitus, Journal of the Japan Diabetes Society, 42, 3835
(1999)."), the IFG, IGT, and IFG/IGT are collectively classified as
borderline diabetes. While a subject with FPG of less than 110
mg/dl would be regarded as NGT by conventional assessment that only
relies on FPG, the subject is categorized, for the first time, into
NGT (OGTT of less than 140 mg/dl), IGT (OGTT of equal to or more
than 140 mg/dl and less than 200 mg/dl), or diabetes (OGTT of equal
to or more than 200 mg/dl) by OGTT measurement.
[0015] Large-scale studies have so far elucidated that a group of
subjects with IGT/diabetes has a greater risk of cardiovascular
disorders compared with a group of subjects with NGT (refer to
"Tominaga, M. et al., The Funagata Diabetes Study, Diabetes Care,
22, 920 (1999)."), which has raised awareness of necessity of OGTT
measurement. Extraction of a group of subjects with IGT/diabetes
that would be missed out if only relying on FPG is important from
the viewpoints of early improvement of lifestyles and early
treatment.
[0016] On the other hand, while importance of OGTT is recognized, a
consultation rate for OGTT in mass screening is low. For example,
although OGTT measurement is normally offered in an overnight-stay
health screening during a comprehensive medical examination, the
consultation rate is approximately one-sixth. Further, OGTT
measurement is rarely conducted in a workplace health screening.
Reasons for this low consultation rate for OGTT include, for
example, burden imposed on a subject who receives consultation
(such as load with high concentration glucose and prolonged
restraint) and operational burden imposed on a person conducting
OGTT measurement. In view of the above circumstance, a simple
measurement method alternative to OGTT is demanded. This simple
measurement method is also useful as a method for pre-screening
those who need to have OGTT measured from among subject who receive
consultation.
[0017] Currently, as a simple assessment method for IGT alternative
to OGTT, a method in which the standard value of FPG is lowered
from 110 mg/dl to 100 mg/dl (refer to "The Expert Committee on the
Diagnosis and Classification of Diabetes Mellitus, Diabetes Care,
26, 3160 (2003).") and a method in which FPG and HbA1c are combined
(refer to "Ko, G. et al., Diabetes Care, 21, 1221 (1998).") have
been proposed. However, because the false-positive rates of these
methods are greater than that of OGTT measurement, a problem is
posed in terms of discrimination capability. Further, adiponectin,
which is a factor of adipokines that are attributable to visceral
fat accumulation, has been also known to be correlated with IGT
(refer to "Wasim, H. et al., Cardiovasc Diabetol., 25, 10
(2006)."); however, this finding has not yet achieved full
reliability in terms of discrimination capability. Furthermore,
although amino acids are known to vary in obesity and diabetes
(refer to "Felig, P., Marliss, E., et al., New Engl. J. Med., 281,
811 (1969)." and "Felig, P., Marliss, E., et al., Diabetes, 19, 727
(1970)."), a concept of IGT had not been introduced at that time
yet, and hence, this finding was not intended to be used to
discriminate IGT.
[0018] As a method for diagnosing a disease state using an amino
acid in the blood, an amino index has been known as described in WO
2004/052191 Pamphlet and WO 2006/098192 Pamphlet. However, in terms
of subjects to be clinically diagnosed, the index is intended to
discriminate between hepatitis C and hepatitis C-free in the
subject in WO 2004/052191 Pamphlet, and it is intended to
discriminate between healthy subjects and patients with ulcerative
colitis as well as healthy subjects and patients with Crohn's
disease in WO 2006/098192 Pamphlet.
[0019] The amino acid metabolism is considered to be affected in
peripheral tissues due to insulin resistance attributable to
visceral fat accumulation, and is considered to be strongly
associated with glucose metabolism, lipid metabolism, inflammatory
reactions, and redox regulatory mechanisms. Therefore, if an amino
acid that varies specifically in, for example, the peripheral
blood, in a group of subjects with IGT could be discovered, and an
index formula employing a concentration parameter of the varied
amino acid could be constructed, such findings are widely
applicable as a simple and sensitive test method that reflects
underlying metabolic alteration in the group of subjects with
IGT.
[0020] However, there is a problem that there are reports on
changes of amino acids in obesity or diabetes, and there is no
report on a metabolic pattern of amino acids in peripheral blood in
an IGT state. Specifically, there is also a problem that there is
no report on application to a diagnostic method for a
discrimination between 2 groups of an IGT and a NGT. There is a
problem that technology of diagnosing the IGT state with a
plurality of amino acids as explanatory variables is not developed
and not practically used.
SUMMARY OF THE INVENTION
[0021] It is an object of the present invention to at least
partially solve the problems in the conventional technology. The
present invention is made in view of the problem described above.
An object of the present invention is to provide a method of
evaluating IGT, an IGT-evaluating apparatus, an IGT-evaluating
method, an IGT-evaluating system, an IGT-evaluating program, and a
recording medium, which are capable of evaluating the IGT state
accurately by utilizing concentrations of amino acids in blood. An
object of the present invention is to provide a method of searching
for prophylactic/ameliorating substance for IGT which is capable of
searching for prophylactic/ameliorating substance for the IGT
efficiently.
[0022] The present inventors have made extensive study for solving
the problems described above, and as a result they have identified
amino acids which are useful in the discrimination between the 2
groups of the IGT and the NGT (specifically, the amino acids
varying with a statistically significant difference between the 2
groups of the IGT and the NGT), and have found that multivariate
discriminants (correlation equations, index formulae) including
concentrations of the identified amino acids as explanatory
variables correlate significantly with a progress of the IGT state,
and the present invention was thereby completed. The present
invention is intended to discriminate between the IGT and the NGT.
In the present invention, IGT and diabetes, in which OGTT is equal
to or more than 140 mg/dl, and which are so-classified by the
classification provided by American Diabetes Association in 1997,
are subjected to evaluation, and these states are collectively
called IGT in the present specification. Further, as described
above, because IGT includes symptoms of hyperglycemia based on
insulin resistance and accompanying risks of cardiovascular
disorders, the present invention is also effective for evaluation
and discrimination of these symptoms. Furthermore, the present
invention can be used in combination with other biological
metabolites, biological indices, and the like as an explanatory
variable besides an amino acid. Moreover, the present invention
enables selection of an existing animal model that partially
reflects a state of IGT and an effective drug in an early stage in
a clinical setting by utilizing information on a typical variation
pattern in the amino acid concentration in IGT and an index formula
corresponding to IGT.
[0023] To solve the problems and achieve the objects described
above, a method of evaluating IGT according to one aspect of the
present invention includes a measuring step of measuring amino acid
concentration data on a concentration value of an amino acid in
blood collected from a subject to be evaluated, and a concentration
value criterion evaluating step of evaluating an impaired glucose
tolerance state in the subject based on the amino acid
concentration data of the subject measured at the measuring
step.
[0024] Another aspect of the present invention is the method of
evaluating IGT, wherein at the concentration value criterion
evaluating step, the impaired glucose tolerance state in the
subject is evaluated based on the concentration value of at least
one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
amino acid concentration data of the subject measured at the
measuring step.
[0025] Still another aspect of the present invention is the method
of evaluating IGT, wherein the concentration value criterion
evaluating step further includes a concentration value criterion
discriminating step of discriminating between an impaired glucose
tolerance and a normal glucose tolerance in the subject based on
the concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the amino acid concentration data of
the subject measured at the measuring step.
[0026] Still another aspect of the present invention is the method
of evaluating IGT, wherein at the concentration value criterion
discriminating step, the discrimination between the impaired
glucose tolerance and the normal glucose tolerance in the subject
is conducted based on the concentration values of at least two of
Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the amino
acid concentration data of the subject measured at the measuring
step.
[0027] Still another aspect of the present invention is the method
of evaluating IGT, wherein the concentration value criterion
evaluating step further includes a discriminant value calculating
step of calculating a discriminant value that is a value of a
multivariate discriminant with a concentration of the amino acid as
an explanatory variable, based on both the amino acid concentration
data of the subject measured at the measuring step and the
previously established multivariate discriminant, and a
discriminant value criterion evaluating step of evaluating the
impaired glucose tolerance state in the subject based on the
discriminant value calculated at the discriminant value calculating
step.
[0028] Still another aspect of the present invention is the method
of evaluating IGT, wherein the multivariate discriminant contains
at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variable, and wherein at the discriminant value
calculating step, the discriminant value is calculated based on
both the concentration value of at least one of Glu, Gly, Ser, Ile,
Val, Leu, Phe and Asp contained in the amino acid concentration
data of the subject measured at the measuring step and the
multivariate discriminant.
[0029] Still another aspect of the present invention is the method
of evaluating IGT, wherein the discriminant value criterion
evaluating step further includes a discriminant value criterion
discriminating step of discriminating between an impaired glucose
tolerance and a normal glucose tolerance in the subject based on
the discriminant value calculated at the discriminant value
calculating step.
[0030] Still another aspect of the present invention is the method
of evaluating IGT, wherein the multivariate discriminant contains
at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variables, and wherein at the discriminant value
calculating step, the discriminant value is calculated based on
both the concentration values of at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject measured at the measuring step
and the multivariate discriminant.
[0031] Still another aspect of the present invention is the method
of evaluating IGT, wherein the multivariate discriminant is
expressed by one fractional expression or the sum of a plurality of
the fractional expressions, and contains at least one of Glu, Ile,
Val, Leu, Phe and Asp as the explanatory variable in the numerator
in the fractional expression constituting the multivariate
discriminant, and at least one of Gly and Ser as the explanatory
variable in the denominator in the fractional expression
constituting the multivariate discriminant, or contains at least
one of Gly and Ser as the explanatory variable in the numerator in
the fractional expression constituting the multivariate
discriminant, and at least one of Glu, Ile, Val, Leu, Phe and Asp
as the explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant.
[0032] Still another aspect of the present invention is the method
of evaluating IGT, wherein the multivariate discriminant is formula
1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0033] Still another aspect of the present invention is the method
of evaluating IGT, wherein the multivariate discriminant is any one
of a logistic regression equation, a linear discriminant, a
multiple regression equation, a discriminant prepared by a support
vector machine, a discriminant prepared by a Mahalanobis'
generalized distance method, a discriminant prepared by canonical
discriminant analysis, and a discriminant prepared by a decision
tree.
[0034] Still another aspect of the present invention is the method
of evaluating IGT, wherein the multivariate discriminant is the
logistic regression equation containing at least Glu and Gly as the
explanatory variables.
[0035] The present invention also relates to an IGT-evaluating
apparatus, the IGT-evaluating apparatus according to one aspect of
the present invention includes a control unit and a memory unit to
evaluate an impaired glucose tolerance state in a subject to be
evaluated. The control unit includes a discriminant
value-calculating unit that calculates a discriminant value that is
a value of a multivariate discriminant with a concentration of an
amino acid as an explanatory variable, based on both previously
obtained amino acid concentration data of the subject on a
concentration value of the amino acid and the multivariate
discriminant stored in the memory unit, and a discriminant value
criterion-evaluating unit that evaluates the impaired glucose
tolerance state in the subject based on the discriminant value
calculated by the discriminant value-calculating unit.
[0036] Another aspect of the present invention is the
IGT-evaluating apparatus, wherein the multivariate discriminant
contains at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
as the explanatory variable, and wherein the discriminant
value-calculating unit calculates the discriminant value based on
both the concentration value of at least one of Glu, Gly, Ser, Ile,
Val, Leu, Phe and Asp contained in the previously obtained amino
acid concentration data of the subject and the multivariate
discriminant.
[0037] Still another aspect of the present invention is the
IGT-evaluating apparatus, wherein the discriminant value
criterion-evaluating unit further includes a discriminant value
criterion-discriminating unit that discriminates between an
impaired glucose tolerance and a normal glucose tolerance in the
subject based on the discriminant value calculated by the
discriminant value-calculating unit.
[0038] Still another aspect of the present invention is the
IGT-evaluating apparatus, wherein the multivariate discriminant
contains at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
as the explanatory variables, and wherein the discriminant
value-calculating unit calculates the discriminant value based on
both the concentration values of at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp contained in the previously obtained
amino acid concentration data of the subject and the multivariate
discriminant.
[0039] Still another aspect of the present invention is the
IGT-evaluating apparatus, wherein the multivariate discriminant is
expressed by one fractional expression or the sum of a plurality of
the fractional expressions, and contains at least one of Glu, Ile,
Val, Leu, Phe and Asp as the explanatory variable in the numerator
in the fractional expression constituting the multivariate
discriminant, and at least one of Gly and Ser as the explanatory
variable in the denominator in the fractional expression
constituting the multivariate discriminant, or contains at least
one of Gly and Ser as the explanatory variable in the numerator in
the fractional expression constituting the multivariate
discriminant, and at least one of Glu, Ile, Val, Leu, Phe and Asp
as the explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant.
[0040] Still another aspect of the present invention is the
IGT-evaluating apparatus, wherein the multivariate discriminant is
formula 1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0041] Still another aspect of the present invention is the
IGT-evaluating apparatus, wherein the multivariate discriminant is
any one of a logistic regression equation, a linear discriminant, a
multiple regression equation, a discriminant prepared by a support
vector machine, a discriminant prepared by a Mahalanobis'
generalized distance method, a discriminant prepared by canonical
discriminant analysis, and a discriminant prepared by a decision
tree.
[0042] Still another aspect of the present invention is the
IGT-evaluating apparatus, wherein the multivariate discriminant is
the logistic regression equation containing at least Glu and Gly as
the explanatory variables.
[0043] Still another aspect of the present invention is the
IGT-evaluating apparatus, wherein the control unit further includes
a multivariate discriminant-preparing unit that prepares the
multivariate discriminant stored in the memory unit, based on
impaired glucose tolerance state information stored in the memory
unit containing the amino acid concentration data and impaired
glucose tolerance state index data on an index for indicating the
impaired glucose tolerance state. The multivariate
discriminant-preparing unit further includes a candidate
multivariate discriminant-preparing unit that prepares a candidate
multivariate discriminant that is a candidate of the multivariate
discriminant, based on a predetermined discriminant-preparing
method from the impaired glucose tolerance state information, a
candidate multivariate discriminant-verifying unit that verifies
the candidate multivariate discriminant prepared by the candidate
multivariate discriminant-preparing unit, based on a predetermined
verifying method, and an explanatory variable-selecting unit that
selects the explanatory variable of the candidate multivariate
discriminant based on a predetermined explanatory
variable-selecting method from a verification result obtained by
the candidate multivariate discriminant-verifying unit, thereby
selecting a combination of the amino acid concentration data
contained in the impaired glucose tolerance state information used
in preparing the candidate multivariate discriminant. The
multivariate discriminant-preparing unit prepares the multivariate
discriminant by selecting the candidate multivariate discriminant
used as the multivariate discriminant, from a plurality of the
candidate multivariate discriminants, based on the verification
results accumulated by repeatedly executing the candidate
multivariate discriminant-preparing unit, the candidate
multivariate discriminant-verifying unit, and the explanatory
variable-selecting unit.
[0044] The present invention also relates to an IGT-evaluating
method, one aspect of the present invention is the IGT-evaluating
method of evaluating an impaired glucose tolerance state in a
subject to be evaluated. The method is carried out with an
information processing apparatus including a control unit and a
memory unit. The method includes (i) a discriminant value
calculating step of calculating a discriminant value that is a
value of a multivariate discriminant with a concentration of an
amino acid as an explanatory variable, based on both previously
obtained amino acid concentration data of the subject on a
concentration value of the amino acid and the multivariate
discriminant stored in the memory unit, and (ii) a discriminant
value criterion evaluating step of evaluating the impaired glucose
tolerance state in the subject based on the discriminant value
calculated at the discriminant value calculating step. The steps
(i) and (ii) are executed by the control unit.
[0045] Another aspect of the present invention is the
IGT-evaluating method, wherein the multivariate discriminant
contains at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
as the explanatory variable, and wherein at the discriminant value
calculating step, the discriminant value is calculated based on
both the concentration value of at least one of Glu, Gly, Ser, Ile,
Val, Leu, Phe and Asp contained in the previously obtained amino
acid concentration data of the subject and the multivariate
discriminant.
[0046] Still another aspect of the present invention is the
IGT-evaluating method, wherein the discriminant value criterion
evaluating step further includes a discriminant value criterion
discriminating step of discriminating between an impaired glucose
tolerance and a normal glucose tolerance in the subject based on
the discriminant value calculated at the discriminant value
calculating step.
[0047] Still another aspect of the present invention is the
IGT-evaluating method, wherein the multivariate discriminant
contains at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
as the explanatory variables, and wherein at the discriminant value
calculating step, the discriminant value is calculated based on
both the concentration values of at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp contained in the previously obtained
amino acid concentration data of the subject and the multivariate
discriminant.
[0048] Still another aspect of the present invention is the
IGT-evaluating method, wherein the multivariate discriminant is
expressed by one fractional expression or the sum of a plurality of
the fractional expressions, and contains at least one of Glu, Ile,
Val, Leu, Phe and Asp as the explanatory variable in the numerator
in the fractional expression constituting the multivariate
discriminant, and at least one of Gly and Ser as the explanatory
variable in the denominator in the fractional expression
constituting the multivariate discriminant, or contains at least
one of Gly and Ser as the explanatory variable in the numerator in
the fractional expression constituting the multivariate
discriminant, and at least one of Glu, Ile, Val, Leu, Phe and Asp
as the explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant.
[0049] Still another aspect of the present invention is the
IGT-evaluating method, wherein the multivariate discriminant is
formula 1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0050] Still another aspect of the present invention is the
IGT-evaluating method, wherein the multivariate discriminant is any
one of a logistic regression equation, a linear discriminant, a
multiple regression equation, a discriminant prepared by a support
vector machine, a discriminant prepared by a Mahalanobis'
generalized distance method, a discriminant prepared by canonical
discriminant analysis, and a discriminant prepared by a decision
tree.
[0051] Still another aspect of the present invention is the
IGT-evaluating method, wherein the multivariate discriminant is the
logistic regression equation containing at least Glu and Gly as the
explanatory variables.
[0052] Still another aspect of the present invention is the
IGT-evaluating method, wherein the method further includes a
multivariate discriminant preparing step of preparing the
multivariate discriminant stored in the memory unit, based on
impaired glucose tolerance state information stored in the memory
unit containing the amino acid concentration data and impaired
glucose tolerance state index data on an index for indicating the
impaired glucose tolerance state. The multivariate discriminant
preparing step is executed by the control unit. The multivariate
discriminant preparing step further includes a candidate
multivariate discriminant preparing step of preparing a candidate
multivariate discriminant that is a candidate of the multivariate
discriminant, based on a predetermined discriminant-preparing
method from the impaired glucose tolerance state information, a
candidate multivariate discriminant verifying step of verifying the
candidate multivariate discriminant prepared at the candidate
multivariate discriminant preparing step, based on a predetermined
verifying method, and an explanatory variable selecting step of
selecting the explanatory variable of the candidate multivariate
discriminant based on a predetermined explanatory
variable-selecting method from a verification result obtained at
the candidate multivariate discriminant verifying step, thereby
selecting a combination of the amino acid concentration data
contained in the impaired glucose tolerance state information used
in preparing the candidate multivariate discriminant. At the
multivariate discriminant preparing step, the multivariate
discriminant is prepared by selecting the candidate multivariate
discriminant used as the multivariate discriminant from a plurality
of the candidate multivariate discriminants, based on the
verification results accumulated by repeatedly executing the
candidate multivariate discriminant preparing step, the candidate
multivariate discriminant verifying step, and the explanatory
variable selecting step.
[0053] The present invention also relates to an IGT-evaluating
system, the IGT-evaluating system according to one aspect of the
present invention includes an IGT-evaluating apparatus including a
control unit and a memory unit to evaluate an impaired glucose
tolerance state in a subject to be evaluated and an information
communication terminal apparatus that provides amino acid
concentration data of the subject on a concentration value of an
amino acid connected to each other communicatively via a network.
The information communication terminal apparatus includes an amino
acid concentration data-sending unit that transmits the amino acid
concentration data of the subject to the IGT-evaluating apparatus,
and an evaluation result-receiving unit that receives an evaluation
result on the impaired glucose tolerance state of the subject
transmitted from the IGT-evaluating apparatus. The control unit of
the IGT-evaluating apparatus includes an amino acid concentration
data-receiving unit that receives the amino acid concentration data
of the subject transmitted from the information communication
terminal apparatus, a discriminant value-calculating unit that
calculates a discriminant value that is a value of a multivariate
discriminant with a concentration of the amino acid as an
explanatory variable, based on both the amino acid concentration
data of the subject received by the amino acid concentration
data-receiving unit and the multivariate discriminant stored in the
memory unit, a discriminant value criterion-evaluating unit that
evaluates the impaired glucose tolerance state in the subject based
on the discriminant value calculated by the discriminant
value-calculating unit, and an evaluation result-sending unit that
transmits the evaluation result of the subject obtained by the
discriminant value criterion-evaluating unit to the information
communication terminal apparatus.
[0054] Another aspect of the present invention is the
IGT-evaluating system, wherein the multivariate discriminant
contains at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
as the explanatory variable, and wherein the discriminant
value-calculating unit calculates the discriminant value based on
both the concentration value of at least one of Glu, Gly, Ser, Ile,
Val, Leu, Phe and Asp contained in the amino acid concentration
data of the subject received by the amino acid concentration
data-receiving unit and the multivariate discriminant.
[0055] Still another aspect of the present invention is the
IGT-evaluating system, wherein the discriminant value
criterion-evaluating unit further includes a discriminant value
criterion-discriminating unit that discriminates between an
impaired glucose tolerance and a normal glucose tolerance in the
subject based on the discriminant value calculated by the
discriminant value-calculating unit.
[0056] Still another aspect of the present invention is the
IGT-evaluating system, wherein the multivariate discriminant
contains at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
as the explanatory variables, and wherein the discriminant
value-calculating unit calculates the discriminant value based on
both the concentration values of at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject received by the amino acid
concentration data-receiving unit and the multivariate
discriminant.
[0057] Still another aspect of the present invention is the
IGT-evaluating system, wherein the multivariate discriminant is
expressed by one fractional expression or the sum of a plurality of
the fractional expressions, and contains at least one of Glu, Ile,
Val, Leu, Phe and Asp as the explanatory variable in the numerator
in the fractional expression constituting the multivariate
discriminant, and at least one of Gly and Ser as the explanatory
variable in the denominator in the fractional expression
constituting the multivariate discriminant, or contains at least
one of Gly and Ser as the explanatory variable in the numerator in
the fractional expression constituting the multivariate
discriminant, and at least one of Glu, Ile, Val, Leu, Phe and Asp
as the explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant.
[0058] Still another aspect of the present invention is the
IGT-evaluating system, wherein the multivariate discriminant is
formula 1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0059] Still another aspect of the present invention is the
IGT-evaluating system, wherein the multivariate discriminant is any
one of a logistic regression equation, a linear discriminant, a
multiple regression equation, a discriminant prepared by a support
vector machine, a discriminant prepared by a Mahalanobis'
generalized distance method, a discriminant prepared by canonical
discriminant analysis, and a discriminant prepared by a decision
tree.
[0060] Still another aspect of the present invention is the
IGT-evaluating system, wherein the multivariate discriminant is the
logistic regression equation containing at least Glu and Gly as the
explanatory variables.
[0061] Still another aspect of the present invention is the
IGT-evaluating system, wherein the control unit of the
IGT-evaluating apparatus further includes a multivariate
discriminant-preparing unit that prepares the multivariate
discriminant stored in the memory unit, based on impaired glucose
tolerance state information stored in the memory unit containing
the amino acid concentration data and impaired glucose tolerance
state index data on an index for indicating the impaired glucose
tolerance state. The multivariate discriminant-preparing unit
further includes a candidate multivariate discriminant-preparing
unit that prepares a candidate multivariate discriminant that is a
candidate of the multivariate discriminant, based on a
predetermined discriminant-preparing method from the impaired
glucose tolerance state information, a candidate multivariate
discriminant-verifying unit that verifies the candidate
multivariate discriminant prepared by the candidate multivariate
discriminant-preparing unit, based on a predetermined verifying
method, and an explanatory variable-selecting unit that selects the
explanatory variable of the candidate multivariate discriminant
based on a predetermined explanatory variable-selecting method from
a verification result obtained by the candidate multivariate
discriminant-verifying unit, thereby selecting a combination of the
amino acid concentration data contained in the impaired glucose
tolerance state information used in preparing the candidate
multivariate discriminant. The multivariate discriminant-preparing
unit prepares the multivariate discriminant by selecting the
candidate multivariate discriminant used as the multivariate
discriminant, from a plurality of the candidate multivariate
discriminants, based on the verification results accumulated by
repeatedly executing the candidate multivariate
discriminant-preparing unit, the candidate multivariate
discriminant-verifying unit, and the explanatory variable-selecting
unit.
[0062] The present invention also relates to an IGT-evaluating
program product, one aspect of the present invention is the
IGT-evaluating program product that makes an information processing
apparatus including a control unit and a memory unit execute a
method of evaluating an impaired glucose tolerance state in a
subject to be evaluated. The method includes (i) a discriminant
value calculating step of calculating a discriminant value that is
a value of a multivariate discriminant with a concentration of an
amino acid as an explanatory variable, based on both previously
obtained amino acid concentration data of the subject on a
concentration value of the amino acid and the multivariate
discriminant stored in the memory unit, and (ii) a discriminant
value criterion evaluating step of evaluating the impaired glucose
tolerance state in the subject based on the discriminant value
calculated at the discriminant value calculating step. The steps
(i) and (ii) are executed by the control unit.
[0063] Another aspect of the present invention is the
IGT-evaluating program product, wherein the multivariate
discriminant contains at least one of Glu, Gly, Ser, Ile, Val, Leu,
Phe and Asp as the explanatory variable, and wherein at the
discriminant value calculating step, the discriminant value is
calculated based on both the concentration value of at least one of
Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
previously obtained amino acid concentration data of the subject
and the multivariate discriminant.
[0064] Still another aspect of the present invention is the
IGT-evaluating program product, wherein the discriminant value
criterion evaluating step further includes a discriminant value
criterion discriminating step of discriminating between an impaired
glucose tolerance and a normal glucose tolerance in the subject
based on the discriminant value calculated at the discriminant
value calculating step.
[0065] Still another aspect of the present invention is the
IGT-evaluating program product, wherein the multivariate
discriminant contains at least two of Glu, Gly, Ser, Ile, Val, Leu,
Phe and Asp as the explanatory variables, and wherein at the
discriminant value calculating step, the discriminant value is
calculated based on both the concentration values of at least two
of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
previously obtained amino acid concentration data of the subject
and the multivariate discriminant.
[0066] Still another aspect of the present invention is the
IGT-evaluating program product, wherein the multivariate
discriminant is expressed by one fractional expression or the sum
of a plurality of the fractional expressions, and contains at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Gly and Ser as the
explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant, or contains
at least one of Gly and Ser as the explanatory variable in the
numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the denominator in the
fractional expression constituting the multivariate
discriminant.
[0067] Still another aspect of the present invention is the
IGT-evaluating program product, wherein the multivariate
discriminant is formula 1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0068] Still another aspect of the present invention is the
IGT-evaluating program product, wherein the multivariate
discriminant is any one of a logistic regression equation, a linear
discriminant, a multiple regression equation, a discriminant
prepared by a support vector machine, a discriminant prepared by a
Mahalanobis' generalized distance method, a discriminant prepared
by canonical discriminant analysis, and a discriminant prepared by
a decision tree.
[0069] Still another aspect of the present invention is the
IGT-evaluating program product, wherein the multivariate
discriminant is the logistic regression equation containing at
least Glu and Gly as the explanatory variables.
[0070] Still another aspect of the present invention is the
IGT-evaluating program product, wherein the method further includes
a multivariate discriminant preparing step of preparing the
multivariate discriminant stored in the memory unit, based on
impaired glucose tolerance state information stored in the memory
unit containing the amino acid concentration data and impaired
glucose tolerance state index data on an index for indicating the
impaired glucose tolerance state. The multivariate discriminant
preparing step is executed by the control unit. The multivariate
discriminant preparing step further includes a candidate
multivariate discriminant preparing step of preparing a candidate
multivariate discriminant that is a candidate of the multivariate
discriminant, based on a predetermined discriminant-preparing
method from the impaired glucose tolerance state information, a
candidate multivariate discriminant verifying step of verifying the
candidate multivariate discriminant prepared at the candidate
multivariate discriminant preparing step, based on a predetermined
verifying method, and an explanatory variable selecting step of
selecting the explanatory variable of the candidate multivariate
discriminant based on a predetermined explanatory
variable-selecting method from a verification result obtained at
the candidate multivariate discriminant verifying step, thereby
selecting a combination of the amino acid concentration data
contained in the impaired glucose tolerance state information used
in preparing the candidate multivariate discriminant. At the
multivariate discriminant preparing step, the multivariate
discriminant is prepared by selecting the candidate multivariate
discriminant used as the multivariate discriminant from a plurality
of the candidate multivariate discriminants, based on the
verification results accumulated by repeatedly executing the
candidate multivariate discriminant preparing step, the candidate
multivariate discriminant verifying step, and the explanatory
variable selecting step.
[0071] The present invention also relates to a recording medium,
the recording medium according to one aspect of the present
invention includes the IGT-evaluating program product described
above.
[0072] The present invention also relates to a method of searching
for prophylactic/ameliorating substance for IGT. One aspect of the
present invention is the method of searching for
prophylactic/ameliorating substance for IGT, wherein the method
includes a measuring step of measuring amino acid concentration
data on a concentration value of an amino acid in blood collected
from a subject to be evaluated to which a desired substance group
consisting of one or more substances that prevent an impaired
glucose tolerance or ameliorate an impaired glucose tolerance state
has been administered, a concentration value criterion evaluating
step of evaluating an impaired glucose tolerance state in the
subject based on the amino acid concentration data measured at the
measuring step, and a judging step of judging whether or not the
desired substance group prevents the impaired glucose tolerance or
ameliorates the impaired glucose tolerance state, based on an
evaluation result obtained at the concentration value criterion
evaluating step.
[0073] Another aspect of the present invention is the method of
searching for prophylactic/ameliorating substance for IGT, wherein
at the concentration value criterion evaluating step, the impaired
glucose tolerance state in the subject is evaluated based on the
concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the amino acid concentration data of
the subject measured at the measuring step.
[0074] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the concentration value criterion evaluating step further
includes a concentration value criterion discriminating step of
discriminating between an impaired glucose tolerance and a normal
glucose tolerance in the subject based on the concentration value
of at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the subject
measured at the measuring step.
[0075] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein at the concentration value criterion discriminating step,
the discrimination between the impaired glucose tolerance and the
normal glucose tolerance in the subject is conducted based on the
concentration values of at least two of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the amino acid concentration data of
the subject measured at the measuring step.
[0076] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the concentration value criterion evaluating step further
includes a discriminant value calculating step of calculating a
discriminant value that is a value of a multivariate discriminant
with a concentration of the amino acid as an explanatory variable,
based on both the amino acid concentration data of the subject
measured at the measuring step and the previously established
multivariate discriminant, and a discriminant value criterion
evaluating step of evaluating the impaired glucose tolerance state
in the subject based on the discriminant value calculated at the
discriminant value calculating step.
[0077] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the multivariate discriminant contains at least one of Glu,
Gly, Ser, Ile, Val, Leu, Phe and Asp as the explanatory variable,
and wherein at the discriminant value calculating step, the
discriminant value is calculated based on both the concentration
value of at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the subject
measured at the measuring step and the multivariate
discriminant.
[0078] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the discriminant value criterion evaluating step further
includes a discriminant value criterion discriminating step of
discriminating between an impaired glucose tolerance and a normal
glucose tolerance in the subject based on the discriminant value
calculated at the discriminant value calculating step.
[0079] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the multivariate discriminant contains at least two of Glu,
Gly, Ser, Ile, Val, Leu, Phe and Asp as the explanatory variables,
and wherein at the discriminant value calculating step, the
discriminant value is calculated based on both the concentration
values of at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the subject
measured at the measuring step and the multivariate
discriminant.
[0080] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the multivariate discriminant is expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and contains at least one of Glu, Ile, Val, Leu, Phe
and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or contains at least one of Gly and Ser
as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant.
[0081] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the multivariate discriminant is formula 1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0082] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the multivariate discriminant is any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree.
[0083] Still another aspect of the present invention is the method
of searching for prophylactic/ameliorating substance for IGT,
wherein the multivariate discriminant is the logistic regression
equation containing at least Glu and Gly as the explanatory
variables.
[0084] According to the method of evaluating IGT of the present
invention, amino acid concentration data on a concentration value
of an amino acid in blood collected from a subject to be evaluated
is measured, and an IGT state in the subject is evaluated based on
the measured amino acid concentration data of the subject. Thus,
concentrations of amino acids in blood can be utilized to bring
about an effect of enabling an accurate evaluation of the IGT
state.
[0085] According to the method of evaluating IGT of the present
invention, the IGT state in the subject is evaluated based on the
concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the measured amino acid concentration
data of the subject. Thus, concentrations of amino acids which
among amino acids in blood, are associated with the IGT state can
be utilized to bring about an effect of enabling an accurate
evaluation of the IGT state.
[0086] According to the method of evaluating IGT of the present
invention, a discrimination between an IGT and a NGT in the subject
is conducted based on the concentration value of at least one of
Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the measured
amino acid concentration data of the subject. Thus, concentrations
of amino acids which among amino acids in blood, are useful for
discriminating between the 2 groups of the IGT and the NGT can be
utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0087] According to the method of evaluating IGT of the present
invention, the discrimination between the IGT and the NGT in the
subject is conducted based on the concentration values of at least
two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
measured amino acid concentration data of the subject. Thus,
concentrations of amino acids which among amino acids in blood, are
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0088] According to the method of evaluating IGT of the present
invention, a discriminant value that is a value of a multivariate
discriminant with a concentration of the amino acid as an
explanatory variable is calculated based on both the measured amino
acid concentration data of the subject and the previously
established multivariate discriminant, and the IGT state in the
subject is evaluated based on the calculated discriminant value.
Thus, discriminant values obtained in multivariate discriminants
with concentrations of amino acids as explanatory variables can be
utilized to bring about an effect of enabling an accurate
evaluation of the IGT state.
[0089] According to the method of evaluating IGT of the present
invention, the discriminant value is calculated based on both the
concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the measured amino acid concentration
data of the subject and the multivariate discriminant containing at
least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variable. Thus, discriminant values obtained in
multivariate discriminants which are correlated with the IGT state
significantly can be utilized to bring about an effect of enabling
an accurate evaluation of the IGT state.
[0090] According to the method of evaluating IGT of the present
invention, a discrimination between an IGT and a NGT in the subject
is conducted based on the calculated discriminant value. Thus,
discriminant values obtained in multivariate discriminants useful
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0091] According to the method of evaluating IGT of the present
invention, the discriminant value is calculated based on both the
concentration values of at least two of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the measured amino acid concentration
data of the subject and the multivariate discriminant containing at
least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variables. Thus, discriminant values obtained in
multivariate discriminants useful for discriminating between the 2
groups of the IGT and the NGT can be utilized to bring about an
effect of enabling an accurate discrimination between the 2 groups
of the IGT and the NGT.
[0092] According to the method of evaluating IGT of the present
invention, the multivariate discriminant is expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and contains at least one of Glu, Ile, Val, Leu, Phe
and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or contains at least one of Gly and Ser
as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Thus, discriminant values obtained in
multivariate discriminants useful particularly for discriminating
between the 2 groups of the IGT and the NGT can be utilized to
bring about an effect of enabling a more accurate discrimination
between the 2 groups of the IGT and the NGT.
[0093] According to the method of evaluating IGT of the present
invention, the multivariate discriminant is formula 1. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0094] According to the method of evaluating IGT of the present
invention, the multivariate discriminant is any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Thus, discriminant
values obtained in multivariate discriminants useful particularly
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling a more accurate
discrimination between the 2 groups of the IGT and the NGT.
[0095] According to the method of evaluating IGT of the present
invention, the multivariate discriminant is the logistic regression
equation containing at least Glu and Gly as the explanatory
variables. Thus, discriminant values obtained in multivariate
discriminants useful particularly for discriminating between the 2
groups of the IGT and the NGT can be utilized to bring about an
effect of enabling a more accurate discrimination between the 2
groups of the IGT and the NGT.
[0096] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, a discriminant value that is a value of a
multivariate discriminant with a concentration of an amino acid as
an explanatory variable is calculated based on both previously
obtained amino acid concentration data of the subject on a
concentration value of the amino acid and the previously
established multivariate discriminant, and an IGT state in the
subject is evaluated based on the calculated discriminant value.
Thus, discriminant values obtained in multivariate discriminants
with concentrations of amino acids as explanatory variables can be
utilized to bring about an effect of enabling an accurate
evaluation of the IGT state.
[0097] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, the discriminant value is calculated based on
both the concentration value of at least one of Glu, Gly, Ser, Ile,
Val, Leu, Phe and Asp contained in the previously obtained amino
acid concentration data of the subject and the multivariate
discriminant containing at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp as the explanatory variable. Thus, discriminant
values obtained in multivariate discriminants which are correlated
with the IGT state significantly can be utilized to bring about an
effect of enabling an accurate evaluation of the IGT state.
[0098] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, a discrimination between an IGT and a NGT in the
subject is conducted based on the calculated discriminant value.
Thus, discriminant values obtained in multivariate discriminants
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0099] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, the discriminant value is calculated based on
both the concentration values of at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp contained in the previously obtained
amino acid concentration data of the subject and the multivariate
discriminant containing at least two of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp as the explanatory variables. Thus, discriminant
values obtained in multivariate discriminants useful for
discriminating between the 2 groups of the IGT and the NGT can be
utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0100] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, the multivariate discriminant is expressed by
one fractional expression or the sum of a plurality of the
fractional expressions, and contains at least one of Glu, Ile, Val,
Leu, Phe and Asp as the explanatory variable in the numerator in
the fractional expression constituting the multivariate
discriminant, and at least one of Gly and Ser as the explanatory
variable in the denominator in the fractional expression
constituting the multivariate discriminant, or contains at least
one of Gly and Ser as the explanatory variable in the numerator in
the fractional expression constituting the multivariate
discriminant, and at least one of Glu, Ile, Val, Leu, Phe and Asp
as the explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0101] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, the multivariate discriminant is formula 1.
Thus, discriminant values obtained in multivariate discriminants
useful particularly for discriminating between the 2 groups of the
IGT and the NGT can be utilized to bring about an effect of
enabling a more accurate discrimination between the 2 groups of the
IGT and the NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0102] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, the multivariate discriminant is any one of a
logistic regression equation, a linear discriminant, a multiple
regression equation, a discriminant prepared by a support vector
machine, a discriminant prepared by a Mahalanobis' generalized
distance method, a discriminant prepared by canonical discriminant
analysis, and a discriminant prepared by a decision tree. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0103] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, the multivariate discriminant is the logistic
regression equation containing at least Glu and Gly as the
explanatory variables. Thus, discriminant values obtained in
multivariate discriminants useful particularly for discriminating
between the 2 groups of the IGT and the NGT can be utilized to
bring about an effect of enabling a more accurate discrimination
between the 2 groups of the IGT and the NGT.
[0104] According to the IGT-evaluating apparatus, the
IGT-evaluating method, and the IGT-evaluating program of the
present invention, the multivariate discriminant stored in the
memory unit is prepared based on IGT state information stored in
the memory unit containing the amino acid concentration data and
IGT state index data on an index for indicating the IGT state.
Specifically, (1) a candidate multivariate discriminant that is a
candidate of the multivariate discriminant is prepared based on a
predetermined discriminant-preparing method from the IGT state
information, (2) the prepared candidate multivariate discriminant
is verified based on a predetermined verifying method, (3) the
explanatory variable of the candidate multivariate discriminant is
selected based on a predetermined explanatory variable-selecting
method from a verification result obtained by executing (2),
thereby selecting a combination of the amino acid concentration
data contained in the IGT state information used in preparing the
candidate multivariate discriminant, and (4) the candidate
multivariate discriminant used as the multivariate discriminant is
selected from a plurality of the candidate multivariate
discriminants based on the verification results accumulated by
repeatedly executing (1), (2) and (3), thereby preparing the
multivariate discriminant. Thus, there can be brought about an
effect of enabling a preparation of multivariate discriminants most
appropriate for evaluating the IGT state (specifically,
multivariate discriminants correlating significantly with the IGT
state (more specifically, multivariate discriminants useful for
discriminating between the 2 groups of the IGT and the NGT)).
[0105] According to the IGT-evaluating system of the present
invention, an information communication terminal apparatus first
transmits amino acid concentration data of a subject to be
evaluated to an IGT-evaluating apparatus. The IGT-evaluating
apparatus receives the amino acid concentration data of the subject
transmitted from the information communication terminal apparatus,
calculates a discriminant value that is a value of a multivariate
discriminant with a concentration of an amino acid as an
explanatory variable, based on both the received amino acid
concentration data of the subject and the multivariate discriminant
stored in a memory unit, and evaluates an IGT state in the subject
based on the calculated discriminant value, and transmits an
evaluation result on the IGT state of the subject to the
information communication terminal apparatus. Then, the information
communication terminal apparatus receives the evaluation result of
the subject transmitted from the IGT-evaluating apparatus. Thus,
discriminant values obtained in multivariate discriminants with
concentrations of amino acids as explanatory variables can be
utilized to bring about an effect of enabling an accurate
evaluation of the IGT state.
[0106] According to the IGT-evaluating system of the present
invention, the discriminant value is calculated based on both the
concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the received amino acid concentration
data of the subject and the multivariate discriminant containing at
least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variable. Thus, discriminant values obtained in
multivariate discriminants which are correlated with the IGT state
significantly can be utilized to bring about an effect of enabling
an accurate evaluation of the IGT state.
[0107] According to the IGT-evaluating system of the present
invention, a discrimination between an IGT and a NGT in the subject
is conducted based on the calculated discriminant value. Thus,
discriminant values obtained in multivariate discriminants useful
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0108] According to the IGT-evaluating system of the present
invention, the discriminant value is calculated based on both the
concentration values of at least two of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the received amino acid concentration
data of the subject and the multivariate discriminant containing at
least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variables. Thus, discriminant values obtained in
multivariate discriminants useful for discriminating between the 2
groups of the IGT and the NGT can be utilized to bring about an
effect of enabling an accurate discrimination between the 2 groups
of the IGT and the NGT.
[0109] According to the IGT-evaluating system of the present
invention, the multivariate discriminant is expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and contains at least one of Glu, Ile, Val, Leu, Phe
and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or contains at least one of Gly and Ser
as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Thus, discriminant values obtained in
multivariate discriminants useful particularly for discriminating
between the 2 groups of the IGT and the NGT can be utilized to
bring about an effect of enabling a more accurate discrimination
between the 2 groups of the IGT and the NGT.
[0110] According to the IGT-evaluating system of the present
invention, the multivariate discriminant is formula 1. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0111] According to the IGT-evaluating system of the present
invention, the multivariate discriminant is any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Thus, discriminant
values obtained in multivariate discriminants useful particularly
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling a more accurate
discrimination between the 2 groups of the IGT and the NGT.
[0112] According to the IGT-evaluating system of the present
invention, the multivariate discriminant is the logistic regression
equation containing at least Glu and Gly as the explanatory
variables. Thus, discriminant values obtained in multivariate
discriminants useful particularly for discriminating between the 2
groups of the IGT and the NGT can be utilized to bring about an
effect of enabling a more accurate discrimination between the 2
groups of the IGT and the NGT.
[0113] According to the IGT-evaluating system of the present
invention, the multivariate discriminant stored in the memory unit
is prepared based on IGT state information stored in the memory
unit containing the amino acid concentration data and IGT state
index data on an index for indicating the IGT state. Specifically,
(1) a candidate multivariate discriminant that is a candidate of
the multivariate discriminant is prepared based on a predetermined
discriminant-preparing method from the IGT state information, (2)
the prepared candidate multivariate discriminant is verified based
on a predetermined verifying method, (3) the explanatory variable
of the candidate multivariate discriminant is selected based on a
predetermined explanatory variable-selecting method from a
verification result obtained by executing (2), thereby selecting a
combination of the amino acid concentration data contained in the
IGT state information used in preparing the candidate multivariate
discriminant, and (4) the candidate multivariate discriminant used
as the multivariate discriminant is selected from a plurality of
the candidate multivariate discriminants based on the verification
results accumulated by repeatedly executing (1), (2) and (3),
thereby preparing the multivariate discriminant. Thus, there can be
brought about an effect of enabling a preparation of multivariate
discriminants most appropriate for evaluating the IGT state
(specifically, multivariate discriminants correlating significantly
with the IGT state (more specifically, multivariate discriminants
useful for discriminating between the 2 groups of the IGT and the
NGT)).
[0114] According to the recording medium of the present invention,
the IGT-evaluating program recorded on the recording medium is read
and executed by the computer, thereby allowing the computer to
execute the IGT-evaluating program, thus bringing about an effect
of obtaining the same effect as in the IGT-evaluating program.
[0115] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, amino acid concentration data on a concentration value
of an amino acid is measured in blood collected from a subject to
be evaluated to which a desired substance group has been
administered, an IGT state in the subject is evaluated based on the
measured amino acid concentration data, and whether or not the
desired substance group prevents an IGT or ameliorates an IGT state
is judged based on an evaluation result. Thus, the method of
evaluating IGT capable of accurately evaluating the IGT state by
utilizing concentrations of amino acids in blood can be used to
bring about an effect of enabling an accurate search for a
substance for preventing the IGT or ameliorating the IGT state.
According to the method of searching for prophylactic/ameliorating
substance for IGT of the present invention, information on amino
acid concentration variation pattern typical of the IGT or a
multivariate discriminant corresponding to the IGT can be used for
selecting a clinically effective chemical at an early stage or an
existing animal model partially reflecting the IGT state.
[0116] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the IGT state in the subject is evaluated based on the
concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the measured amino acid concentration
data of the subject. Thus, concentrations of amino acids which
among amino acids in blood, are associated with the IGT state can
be utilized to bring about an effect of enabling an accurate
evaluation of the IGT state.
[0117] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, a discrimination between an IGT and a NGT in the subject
is conducted based on the concentration value of at least one of
Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the measured
amino acid concentration data of the subject. Thus, concentrations
of amino acids which among amino acids in blood, are useful for
discriminating between the 2 groups of the IGT and the NGT can be
utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0118] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the discrimination between the IGT and the NGT in the
subject is conducted based on the concentration values of at least
two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
measured amino acid concentration data of the subject. Thus,
concentrations of amino acids which among amino acids in blood, are
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0119] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, a discriminant value that is a value of a multivariate
discriminant with a concentration of the amino acid as an
explanatory variable is calculated based on both the measured amino
acid concentration data of the subject and the previously
established multivariate discriminant, and the IGT state in the
subject is evaluated based on the calculated discriminant value.
Thus, discriminant values obtained in multivariate discriminants
with concentrations of amino acids as explanatory variables can be
utilized to bring about an effect of enabling an accurate
evaluation of the IGT state.
[0120] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the discriminant value is calculated based on both the
concentration value of at least one of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the measured amino acid concentration
data of the subject and the multivariate discriminant containing at
least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variable. Thus, discriminant values obtained in
multivariate discriminants which are correlated with the IGT state
significantly can be utilized to bring about an effect of enabling
an accurate evaluation of the IGT state.
[0121] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, a discrimination between an IGT and a NGT in the subject
is conducted based on the calculated discriminant value. Thus,
discriminant values obtained in multivariate discriminants useful
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0122] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the discriminant value is calculated based on both the
concentration values of at least two of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp contained in the measured amino acid concentration
data of the subject and the multivariate discriminant containing at
least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variables. Thus, discriminant values obtained in
multivariate discriminants useful for discriminating between the 2
groups of the IGT and the NGT can be utilized to bring about an
effect of enabling an accurate discrimination between the 2 groups
of the IGT and the NGT.
[0123] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the multivariate discriminant is expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and contains at least one of Glu, Ile, Val, Leu, Phe
and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or contains at least one of Gly and Ser
as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Thus, discriminant values obtained in
multivariate discriminants useful particularly for discriminating
between the 2 groups of the IGT and the NGT can be utilized to
bring about an effect of enabling a more accurate discrimination
between the 2 groups of the IGT and the NGT.
[0124] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the multivariate discriminant is formula 1. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0125] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the multivariate discriminant is any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Thus, discriminant
values obtained in multivariate discriminants useful particularly
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling a more accurate
discrimination between the 2 groups of the IGT and the NGT.
[0126] According to the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention, the multivariate discriminant is the logistic regression
equation containing at least Glu and Gly as the explanatory
variables. Thus, discriminant values obtained in multivariate
discriminants useful particularly for discriminating between the 2
groups of the IGT and the NGT can be utilized to bring about an
effect of enabling a more accurate discrimination between the 2
groups of the IGT and the NGT.
[0127] When the IGT state is evaluated (specifically, the
discrimination between the IGT group and the NGT group is
conducted) in the present invention, concentrations of other
metabolites (biological metabolites), protein expression level, age
and sex of the subject, biological indices or the like may be used
in addition to the concentrations of the amino acids. When the IGT
state is evaluated (specifically, the discrimination between the
IGT group and the NGT group is conducted) in the present invention,
concentrations of other metabolites (biological metabolites),
protein expression level, age and sex of the subject, biological
indices or the like may be used as the explanatory variables in the
multivariate discriminants in addition to the concentrations of the
amino acids.
[0128] The IGT includes symptoms of hyperglycemia based on insulin
resistance and accompanying risks of cardiovascular disorders, and
thus the present invention is also effective in evaluation or
discrimination thereof.
[0129] The above and other objects, features, advantages and
technical and industrial significance of this invention will be
better understood by reading the following detailed description of
presently preferred embodiments of the invention, when considered
in connection with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0130] FIG. 1 is a principle configurational diagram showing a
basic principle of the present invention;
[0131] FIG. 2 is a flowchart showing one example of a method of
evaluating IGT according to a first embodiment;
[0132] FIG. 3 is a principle configurational diagram showing a
basic principle of the present invention;
[0133] FIG. 4 is a diagram showing an example of an entire
configuration of a present system;
[0134] FIG. 5 is a diagram showing another example of an entire
configuration of the present system;
[0135] FIG. 6 is a block diagram showing an example of a
configuration of an IGT-evaluating apparatus 100 in the present
system;
[0136] FIG. 7 is a chart showing an example of information stored
in a user information file 106a;
[0137] FIG. 8 is a chart showing an example of information stored
in an amino acid concentration data file 106b;
[0138] FIG. 9 is a chart showing an example of information stored
in an IGT state information file 106c;
[0139] FIG. 10 is a chart showing an example of information stored
in a designated IGT state information file 106d;
[0140] FIG. 11 is a chart showing an example of information stored
in a candidate multivariate discriminant file 106e1;
[0141] FIG. 12 is a chart showing an example of information stored
in a verification result file 106e2;
[0142] FIG. 13 is a chart showing an example of information stored
in a selected IGT state information file 106e3;
[0143] FIG. 14 is a chart showing an example of information stored
in a multivariate discriminant file 106e4;
[0144] FIG. 15 is a chart showing an example of information stored
in a discriminant value file 106f;
[0145] FIG. 16 is a chart showing an example of information stored
in an evaluation result file 106g;
[0146] FIG. 17 is a block diagram showing a configuration of a
multivariate discriminant-preparing part 102h;
[0147] FIG. 18 is a block diagram showing a configuration of a
discriminant value criterion-evaluating part 102j;
[0148] FIG. 19 is a block diagram showing an example of a
configuration of a client apparatus 200 in the present system;
[0149] FIG. 20 is a block diagram showing an example of a
configuration of a database apparatus 400 in the present
system;
[0150] FIG. 21 is a flowchart showing an example of an IGT
evaluation service processing performed in the present system;
[0151] FIG. 22 is a flowchart showing an example of a multivariate
discriminant-preparing processing performed in the IGT-evaluating
apparatus 100 in the present system;
[0152] FIG. 23 is a principle configurational diagram showing a
basic principle of the present invention;
[0153] FIG. 24 is a flowchart showing one example of a method of
searching for prophylactic/ameliorating substance for IGT according
to a third embodiment;
[0154] FIG. 25 is boxplots showing distributions of amino acid
explanatory variables between 2 groups of NGT and IGT;
[0155] FIG. 26 is a graph showing ROC curve for an evaluation of a
discrimination capability between 2 groups;
[0156] FIG. 27 is a graph showing ROC curve for an evaluation of a
discrimination capability between 2 groups;
[0157] FIG. 28 is a chart showing a list of AUCs of ROC curves for
an evaluation of a discrimination capability between 2 groups;
[0158] FIG. 29 is a chart showing a list of AUCs of ROC curves for
an evaluation of a discrimination capability between 2 groups;
[0159] FIG. 30 is a chart showing a list of AUCs of ROC curves for
an evaluation of a discrimination capability between 2 groups;
[0160] FIG. 31 is a chart showing a list of AUCs of ROC curves for
an evaluation of a discrimination capability between 2 groups;
[0161] FIG. 32 is a chart showing a list of logistic regression
equations;
[0162] FIG. 33 is a chart showing a list of logistic regression
equations;
[0163] FIG. 34 is a diagram showing discriminatory conditions of a
discrimination between 2 groups of NGT and IGT;
[0164] FIG. 35 is a chart showing a list of AUCs of ROC curves for
an evaluation of a discrimination capability between 2 groups;
[0165] FIG. 36 is a chart showing a list of AUCs of ROC curves for
an evaluation of a discrimination capability between 2 groups;
[0166] FIG. 37 is a chart showing a list of AUCs of ROC curves and
fractional expressions with real coefficients;
[0167] FIG. 38 is a chart showing a list of AUCs of ROC curves and
fractional expressions with real coefficients;
[0168] FIG. 39 is a chart showing a list of AUCs of ROC curves and
logistic regression equations containing numerical coefficients;
and
[0169] FIG. 40 is a chart showing a list of AUCs of ROC curves and
logistic regression equations containing numerical
coefficients.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0170] Hereinafter, an embodiment (first embodiment) of the method
of evaluating IGT of the present invention, an embodiment (second
embodiment) of the IGT-evaluating apparatus, the IGT-evaluating
method, the IGT-evaluating system, the IGT-evaluating program and
the recording medium of the present invention, and an embodiment
(third embodiment) of the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention are described in detail with reference to the drawings.
The present invention is not limited to these embodiments.
First Embodiment
1-1. Outline of the Invention
[0171] Here, an outline of the method of evaluating IGT of the
present invention will be described with reference to FIG. 1. FIG.
1 is a principle configurational diagram showing a basic principle
of the present invention.
[0172] Amino acid concentration data on a concentration value of an
amino acid in blood collected from a subject (for example, an
individual such as animal or human) to be evaluated are first
measured (step S-11). Concentrations of amino acids in blood are
analyzed in the following manner. A blood sample is collected in a
heparin-treated tube, and then the blood plasma is separated by
centrifugation of the collected blood sample. All blood plasma
samples separated are frozen and stored at -70.degree. C. before a
measurement of amino acid concentrations. Before the measurement of
amino acid concentrations, the blood plasma samples are
deproteinized by adding sulfosalicylic acid to a concentration of
3%. An amino acid analyzer by high-performance liquid
chromatography (HPLC) by using ninhydrin reaction in post column is
used for the measurement of amino acid concentrations. The unit of
the amino acid concentration may be for example molar
concentration, weight concentration, or these concentrations which
are subjected to addition, subtraction, multiplication and division
by an arbitrary constant.
[0173] An IGT state in the subject is evaluated based on the amino
acid concentration data of the subject measured in the step S-11
(step S-12).
[0174] According to the present invention described above, the
amino acid concentration data on the concentration value of the
amino acid in blood collected from the subject is measured, and the
IGT state in the subject is evaluated based on the measured amino
acid concentration data of the subject. Thus, concentrations of
amino acids in blood can be utilized to bring about an effect of
enabling an accurate evaluation of the IGT state.
[0175] Before the step S-12 is executed, data such as defective and
outliers may be removed from the amino acid concentration data of
the subject measured in the step S-11. Thus, the IGT state can be
more accurately evaluated.
[0176] In the step S-12, the IGT state in the subject may be
evaluated based on the concentration value of at least one of Glu,
Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject measured in the step S-11. Thus,
concentrations of amino acids which among amino acids in blood, are
associated with the IGT state can be utilized to bring about an
effect of enabling an accurate evaluation of the IGT state.
[0177] In the step S-12, a discrimination between an IGT and a NGT
in the subject may be conducted based on the concentration value of
at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained
in the amino acid concentration data of the subject measured in the
step S-11. Specifically, the concentration value of at least one of
Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp may be compared with a
previously established threshold (cutoff value), thereby
discriminating between the IGT and the NGT in the subject. Thus,
concentrations of amino acids which among amino acids in blood, are
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0178] In the step S-12, the discrimination between the IGT and the
NGT in the subject may be conducted based on the concentration
values of at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the subject
measured in the step S-11. Specifically, the concentration values
of at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp may be
compared with a previously established threshold (cutoff value),
thereby discriminating between the IGT and the NGT in the subject.
Thus, concentrations of amino acids which among amino acids in
blood, are useful for discriminating between the 2 groups of the
IGT and the NGT can be utilized to bring about an effect of
enabling an accurate discrimination between the 2 groups of the IGT
and the NGT.
[0179] In the step S-12, a discriminant value that is a value of a
multivariate discriminant with a concentration of the amino acid as
an explanatory variable may be calculated based on both the amino
acid concentration data of the subject measured in the step S-11
and the previously established multivariate discriminant, and the
IGT state in the subject may be evaluated based on the calculated
discriminant value. Thus, discriminant values obtained in
multivariate discriminants with concentrations of amino acids as
explanatory variables can be utilized to bring about an effect of
enabling an accurate evaluation of the IGT state.
[0180] In the step S-12, the discriminant value may be calculated
based on both the concentration value of at least one of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject measured in the step S-11 and the
multivariate discriminant containing at least one of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variable, and the IGT
state in the subject may be evaluated based on the calculated
discriminant value. Thus, discriminant values obtained in
multivariate discriminants which are correlated with the IGT state
significantly can be utilized to bring about an effect of enabling
an accurate evaluation of the IGT state.
[0181] In the step S-12, the discrimination between the IGT and the
NGT in the subject may be conducted based on the calculated
discriminant value. Specifically, the discriminant value may be
compared with a previously established threshold (cutoff value),
thereby discriminating between the IGT and the NGT in the subject.
Thus, discriminant values obtained in multivariate discriminants
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0182] In the step S-12, the discriminant value may be calculated
based on both the concentration values of at least two of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject measured in the step S-11 and the
multivariate discriminant containing at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variables, and the
discrimination between the IGT and the NGT in the subject may be
conducted based on the calculated discriminant value. Thus,
discriminant values obtained in multivariate discriminants useful
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0183] the multivariate discriminant may be expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and may contain at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or may contain at least one of Gly and
Ser as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Specifically, the multivariate
discriminant may be formula 1. Thus, discriminant values obtained
in multivariate discriminants useful particularly for
discriminating between the 2 groups of the IGT and the NGT can be
utilized to bring about an effect of enabling a more accurate
discrimination between the 2 groups of the IGT and the NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0184] the multivariate discriminant may be any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Specifically, the
multivariate discriminant may be the logistic regression equation
containing at least Glu and Gly as the explanatory variables. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0185] The multivariate discriminants described above can be
prepared by a method described in International Publication WO
2004/052191 Pamphlet that is an international application filed by
the present applicant or by a method (multivariate
discriminant-preparing processing described in the second
embodiment described later) described in International Publication
WO 2006/098192 Pamphlet that is an international application filed
by the present applicant. Any multivariate discriminants obtained
by these methods can be preferably used in the evaluation of the
IGT state, regardless of the unit of the amino acid concentration
in the amino acid concentration data as input data.
[0186] In the fractional expression, the numerator of the
fractional expression is expressed by the sum of the amino acids A,
B, C etc. and the denominator of the fractional expression is
expressed by the sum of the amino acids a, b, c etc. The fractional
expression also includes the sum of the fractional expressions
.alpha., .beta., .gamma. etc. (for example, .alpha.+.beta.) having
such constitution. The fractional expression also includes divided
fractional expressions. The amino acids used in the numerator or
denominator may have suitable coefficients respectively. The amino
acids used in the numerator or denominator may appear repeatedly.
Each fractional expression may have a suitable coefficient. A value
of a coefficient for each explanatory variable and a value for a
constant term may be any real numbers. In combinations where
explanatory variables in the numerator and explanatory variables in
the denominator in the fractional expression are switched with each
other, the positive (or negative) sign is generally reversed in
correlation with objective explanatory variables, but because their
correlation is maintained, such combinations can be assumed to be
equivalent to one another in discrimination, and thus the
fractional expression also includes combinations where explanatory
variables in the numerator and explanatory variables in the
denominator in the fractional expression are switched with each
other.
[0187] The multivariate discriminant refers to a form of equation
used generally in multivariate analysis and includes, for example,
multiple regression equation, multiple logistic regression
equation, linear discriminant function, Mahalanobis' generalized
distance, canonical discriminant function, support vector machine,
and decision tree. The multivariate discriminant also includes an
equation shown by the sum of different forms of multivariate
discriminants. In the multiple regression equation, multiple
logistic regression equation and canonical discriminant function, a
coefficient and constant term are added to each explanatory
variable, and the coefficient and constant term in this case are
preferably real numbers, more preferably values in the range of 99%
confidence interval for the coefficient and constant term obtained
from data for discrimination, more preferably in the range of 95%
confidence interval for the coefficient and constant term obtained
from data for discrimination. The value of each coefficient and the
confidence interval thereof may be those multiplied by a real
number, and the value of each constant term and the confidence
interval thereof may be those having an arbitrary actual constant
added or subtracted or those multiplied or divided by an arbitrary
actual constant. When an expression such as a logistic regression,
a linear discriminant, and a multiple regression analysis is used
as an index, a linear transformation of the expression (addition of
a constant and multiplication by a constant) and a monotonic
increasing (decreasing) transformation (for example, a logit
transformation) of the expression do not alter discrimination
capability, and thus are equivalent. Therefore, the expression
includes an expression that is subjected to a linear transformation
and a monotonic increasing (decreasing) transformation.
[0188] When the IGT state is evaluated (specifically, the
discrimination between the IGT and the NGT is conducted) in the
present invention, concentrations of other metabolites (biological
metabolites), protein expression level, age and sex of the subject,
biological indices or the like may be used in addition to the
concentrations of the amino acids. When the IGT state is evaluated
(specifically, the discrimination between the IGT and the NGT is
conducted) in the present invention, concentrations of other
metabolites (biological metabolites), protein expression level, age
and sex of the subject, biological indices or the like may be used
as the explanatory variables in the multivariate discriminants in
addition to the concentrations of the amino acids.
1-2. Method of Evaluating IGT in Accordance with the First
Embodiment
[0189] Herein, the method of evaluating IGT according to the first
embodiment is described with reference to FIG. 2. FIG. 2 is a
flowchart showing one example of the method of evaluating IGT
according to the first embodiment.
[0190] The amino acid concentration data on the concentration
values of the amino acids is measured from blood collected from an
individual such as animal or human (step SA-11). The measurement of
the concentration values of the amino acids is conducted by the
method described above.
[0191] Data such as defective and outliers is then removed from the
amino acid concentration data of the individual measured in the
step SA-11 (step SA-12).
[0192] Then, the concentration value of at least one or at least
two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
amino acid concentration data of the individual from which the data
such as the defective and outliers have been removed in the step
SA-12 is compared with a previously established threshold (cutoff
value), thereby discriminating between the IGT and the NGT in the
individual (step SA-13).
1-3. Summary of the First Embodiment and Other Embodiments
[0193] According to the method of evaluating IGT according to the
first embodiment described in detail above, (1) the amino acid
concentration data is measured from blood collected from the
individual, (2) the data such as the defective and the outliers is
removed from the measured amino acid concentration data of the
individual, and (3) the concentration value of at least one or at
least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in
the amino acid concentration data of the individual from which the
data such as the defective and the outliers have been removed is
compared with the previously established threshold (cutoff value),
thereby discriminating between the IGT and the NGT in the
individual. Thus, concentrations of amino acids which among amino
acids in blood, are useful for discriminating between the 2 groups
of the IGT and the NGT can be utilized to bring about an effect of
enabling an accurate discrimination between the 2 groups of the IGT
and the NGT.
[0194] In the step SA-13, (a) the discriminant value may be
calculated based on both (i) the concentration value of at least
one or at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the individual
from which the data such as the defective and the outliers have
been removed in the step SA-12 and (ii) the multivariate
discriminant containing at least one or at least two of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp as the explanatory variable, and
(b) the calculated discriminant value may be compared with the
previously established threshold (cutoff value), thereby
discriminating between the IGT and the NGT in the individual. Thus,
discriminant values obtained in multivariate discriminants useful
for discriminating between the 2 groups of the IGT and the NGT can
be utilized to bring about an effect of enabling an accurate
discrimination between the 2 groups of the IGT and the NGT.
[0195] In the step SA-13, the multivariate discriminant may be
expressed by one fractional expression or the sum of a plurality of
the fractional expressions, and may contain at least one of Glu,
Ile, Val, Leu, Phe and Asp as the explanatory variable in the
numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Gly and Ser as the
explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant, or may
contain at least one of Gly and Ser as the explanatory variable in
the numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the denominator in the
fractional expression constituting the multivariate discriminant.
Specifically, the multivariate discriminant may be formula 1. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
In the step SA-13, the multivariate discriminant may be any one of
a logistic regression equation, a linear discriminant, a multiple
regression equation, a discriminant prepared by a support vector
machine, a discriminant prepared by a Mahalanobis' generalized
distance method, a discriminant prepared by canonical discriminant
analysis, and a discriminant prepared by a decision tree.
Specifically, the multivariate discriminant may be the logistic
regression equation containing at least Glu and Gly as the
explanatory variables. Thus, discriminant values obtained in
multivariate discriminants useful particularly for discriminating
between the 2 groups of the IGT and the NGT can be utilized to
bring about an effect of enabling a more accurate discrimination
between the 2 groups of the IGT and the NGT.
[0196] The multivariate discriminants described above can be
prepared by a method described in International Publication WO
2004/052191 Pamphlet that is an international application filed by
the present applicant or by a method (multivariate
discriminant-preparing processing described in the second
embodiment described later) described in International Publication
WO 2006/098192 Pamphlet that is an international application filed
by the present applicant. Any multivariate discriminants obtained
by these methods can be preferably used in the evaluation of the
IGT state, regardless of the unit of the amino acid concentration
in the amino acid concentration data as input data.
Second Embodiment
2-1. Outline of the Invention
[0197] Herein, an outline of the IGT-evaluating apparatus, the
IGT-evaluating method, the IGT-evaluating system, the
IGT-evaluating program and the recording medium of the present
invention are described in detail with reference to FIG. 3. FIG. 3
is a principle configurational diagram showing a basic principle of
the present invention.
[0198] In the present invention, a discriminant value that is a
value of a multivalent discriminant with a concentration of an
amino acid as an explanatory variable is calculated in a control
device based on both previously obtained amino acid concentration
data of a subject to be evaluated (for example, an individual such
as animal or human) and the multivariate discriminant stored in a
memory device (step S-21).
[0199] In the present invention, an IGT state in the subject is
evaluated in the control device based on the discriminant value
calculated in the step S-21 (step S-22).
[0200] According to the present invention described above, the
discriminant value is calculated based on both the previously
obtained amino acid concentration data of the subject on the
concentration value of the amino acid and the multivariate
discriminant with the concentration of the amino acid as the
explanatory variable stored in the memory device, and the IGT state
in the subject is evaluated based on the calculated discriminant
value. Thus, discriminant values obtained in multivariate
discriminants with concentrations of amino acids as explanatory
variables can be utilized to bring about an effect of enabling an
accurate evaluation of the IGT state.
[0201] In the step S-21, the discriminant value may be calculated
based on both the concentration value of at least one of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp contained in the previously
obtained amino acid concentration data of the subject and the
multivariate discriminant containing at least one of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variable. Thus,
discriminant values obtained in multivariate discriminants which
are correlated with the IGT state significantly can be utilized to
bring about an effect of enabling an accurate evaluation of the IGT
state.
[0202] In the step S-22, a discrimination between an IGT and a NGT
in the subject may be conducted based on the discriminant value
calculated in the step S-21. Specifically, the discriminant value
may be compared with a previously established threshold (cutoff
value), thereby discriminating between the IGT and the NGT in the
subject. Thus, discriminant values obtained in multivariate
discriminants useful for discriminating between the 2 groups of the
IGT and the NGT can be utilized to bring about an effect of
enabling an accurate discrimination between the 2 groups of the IGT
and the NGT.
[0203] In the step S-21, the discriminant value may be calculated
based on both the concentration values of at least two of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp contained in the previously
obtained amino acid concentration data of the subject and the
multivariate discriminant containing at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variables. In the
step S-22, the discrimination between the IGT and the NGT in the
subject may be conducted based on the discriminant value calculated
in the step S-21. Thus, discriminant values obtained in
multivariate discriminants useful for discriminating between the 2
groups of the IGT and the NGT can be utilized to bring about an
effect of enabling an accurate discrimination between the 2 groups
of the IGT and the NGT.
[0204] The multivariate discriminant may be expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and may contain at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or may contain at least one of Gly and
Ser as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Specifically, the multivariate
discriminant may be formula 1. Thus, discriminant values obtained
in multivariate discriminants useful particularly for
discriminating between the 2 groups of the IGT and the NGT can be
utilized to bring about an effect of enabling a more accurate
discrimination between the 2 groups of the IGT and the NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0205] The multivariate discriminant may be any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Specifically, the
multivariate discriminant may be the logistic regression equation
containing at least Glu and Gly as the explanatory variables. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0206] The multivariate discriminants described above can be
prepared by a method described in International Publication WO
2004/052191 Pamphlet that is an international application filed by
the present applicant or by a method (multivariate
discriminant-preparing processing described later) described in
International Publication WO 2006/098192 Pamphlet that is an
international application filed by the present applicant. Any
multivariate discriminants obtained by these methods can be
preferably used in the evaluation of the IGT state, regardless of
the unit of the amino acid concentration in the amino acid
concentration data as input data.
[0207] In the fractional expression, the numerator of the
fractional expression is expressed by the sum of the amino acids A,
B, C etc. and the denominator of the fractional expression is
expressed by the sum of the amino acids a, b, c etc. The fractional
expression also includes the sum of the fractional expressions
.alpha., .beta., .gamma. etc. (for example, .alpha.+.beta.) having
such constitution. The fractional expression also includes divided
fractional expressions. The amino acids used in the numerator or
denominator may have suitable coefficients respectively. The amino
acids used in the numerator or denominator may appear repeatedly.
Each fractional expression may have a suitable coefficient. A value
of a coefficient for each explanatory variable and a value for a
constant term may be any real numbers. In combinations where
explanatory variables in the numerator and explanatory variables in
the denominator in the fractional expression are switched with each
other, the positive (or negative) sign is generally reversed in
correlation with objective explanatory variables, but because their
correlation is maintained, such combinations can be assumed to be
equivalent to one another in discrimination, and thus the
fractional expression also includes combinations where explanatory
variables in the numerator and explanatory variables in the
denominator in the fractional expression are switched with each
other.
[0208] The multivariate discriminant refers to a form of equation
used generally in multivariate analysis and includes, for example,
multiple regression equation, multiple logistic regression
equation, linear discriminant function, Mahalanobis' generalized
distance, canonical discriminant function, support vector machine,
and decision tree. The multivariate discriminant also includes an
equation shown by sum of different forms of the multivariate
discriminants. In the multiple regression equation, multiple
logistic regression equation and canonical discriminant function, a
coefficient and constant term are added to each explanatory
variable, and the coefficient and constant term in this case are
preferably real numbers, more preferably values in the range of 99%
confidence interval for the coefficient and constant term obtained
from data for discrimination, more preferably in the range of 95%
confidence interval for the coefficient and constant term obtained
from data for discrimination. The value of each coefficient and the
confidence interval thereof may be those multiplied by a real
number, and the value of each constant term and the confidence
interval thereof may be those having an arbitrary actual constant
added or subtracted or those multiplied or divided by an arbitrary
actual constant. When an expression such as a logistic regression,
a linear discriminant, and a multiple regression analysis is used
as an index, a linear transformation of the expression (addition of
a constant and multiplication by a constant) and a monotonic
increasing (decreasing) transformation (for example, a logit
transformation) of the expression do not alter discrimination
capability, and thus are equivalent. Therefore, the expression
includes an expression that is subjected to a linear transformation
and a monotonic increasing (decreasing) transformation.
[0209] When the IGT state is evaluated (specifically, the
discrimination between the IGT and the NGT is conducted) in the
present invention, concentrations of other metabolites (biological
metabolites), protein expression level, age and sex of the subject,
biological indices or the like may be used in addition to the
concentrations of the amino acids. When the IGT state is evaluated
(specifically, the discrimination between the IGT and the NGT is
conducted) in the present invention, concentrations of other
metabolites (biological metabolites), protein expression level, age
and sex of the subject, biological indices or the like may be used
as the explanatory variables in the multivariate discriminants in
addition to the concentrations of the amino acids.
[0210] Here, the summary of the multivariate discriminant-preparing
processing (steps 1 to 4) is described in detail.
[0211] First, a candidate multivariate discriminant (e.g.,
y=a.sub.1x.sub.1+a.sub.2x.sub.2+ . . . . +a.sub.nx.sub.n, y: IGT
state index data, x.sub.i: amino acid concentration data, a.sub.i:
constant, i=1, 2, . . . , n) that is a candidate of the
multivariate discriminant is prepared in the control device based
on a predetermined discriminant-preparing method from IGT state
information stored in the memory device containing the amino acid
concentration data and IGT state index data on an index for
indicating the IGT state (step 1). Data containing defective and
outliers may be removed in advance from the IGT state
information.
[0212] In the step 1, a plurality of the candidate multivariate
discriminants may be prepared from the IGT state information by
using a plurality of the different discriminant-preparing methods
(including those for multivariate analysis such as principal
component analysis, discriminant analysis, support vector machine,
multiple regression analysis, logistic regression analysis, k-means
method, cluster analysis, and decision tree). Specifically, a
plurality of the candidate multivariate discriminant groups may be
prepared simultaneously and concurrently by using a plurality of
different algorithms with the IGT state information which is
multivariate data composed of the amino acid concentration data and
the IGT state index data obtained by analyzing blood samples from a
large number of NGT groups and IGT groups. For example, the two
different candidate multivariate discriminants may be formed by
performing discriminant analysis and logistic regression analysis
simultaneously with the different algorithms. Alternatively, the
candidate multivariate discriminant may be formed by converting the
IGT state information with the candidate multivariate discriminant
prepared by performing principal component analysis and then
performing discriminant analysis of the converted IGT state
information. In this way, it is possible to finally prepare the
multivariate discriminant suitable for diagnostic condition.
[0213] The candidate multivariate discriminant prepared by
principal component analysis is a linear expression consisting of
amino acid explanatory variables maximizing the variance of all
amino acid concentration data. The candidate multivariate
discriminant prepared by discriminant analysis is a high-powered
expression (including exponential and logarithmic expressions)
consisting of amino acid explanatory variables minimizing the ratio
of the sum of the variances in respective groups to the variance of
all amino acid concentration data. The candidate multivariate
discriminant prepared by using support vector machine is a
high-powered expression (including kernel function) consisting of
amino acid explanatory variables maximizing the boundary between
groups. The candidate multivariate discriminant prepared by
multiple regression analysis is a high-powered expression
consisting of amino acid explanatory variables minimizing the sum
of the distances from all amino acid concentration data. The
candidate multivariate discriminant prepared by logistic regression
analysis is a fraction expression having, as a component, the
natural logarithm having a linear expression consisting of amino
acid explanatory variables maximizing the likelihood as the
exponent. The k-means method is a method of searching k pieces of
neighboring amino acid concentration data in various groups,
designating the group containing the greatest number of the
neighboring points as its data-belonging group, and selecting the
amino acid explanatory variable that makes the group to which input
amino acid concentration data belong agree well with the designated
group. The cluster analysis is a method of clustering (grouping)
the points closest in entire amino acid concentration data. The
decision tree is a method of ordering amino acid explanatory
variables and predicting the group of amino acid concentration data
from the pattern possibly held by the higher-ordered amino acid
explanatory variable.
[0214] Returning to the description of the multivariate
discriminant-preparing processing, the candidate multivariate
discriminant prepared in the step 1 is verified (mutually verified)
in the control device based on a predetermined verifying method
(step 2). The verification of the candidate multivariate
discriminant is performed on each other to each candidate
multivariate discriminant prepared in the step 1.
[0215] In the step 2, at least one of discrimination rate,
sensitivity, specificity, information criterion, and the like of
the candidate multivariate discriminant may be verified by at least
one of the bootstrap method, holdout method, leave-one-out method,
and the like. In this way, it is possible to prepare the candidate
multivariate discriminant higher in predictability or reliability,
by taking the IGT state information and the diagnostic condition
into consideration.
[0216] The discrimination rate is the rate of the IGT states judged
correct according to the present invention in all input data. The
sensitivity is the rate of the IGT states judged correct according
to the present invention in the IGT states declared in the input
data. The specificity is the rate of the IGT states judged correct
according to the present invention in the NGT declared in the input
data. The information criterion is the sum of the number of the
amino acid explanatory variables in the candidate multivariate
discriminant prepared in the step 1 and the difference in number
between the IGT states evaluated according to the present invention
and those declared in input data. The predictability is the average
of the discrimination rate, sensitivity, or specificity obtained by
repeating verification of the candidate multivariate discriminant.
Alternatively, the reliability is the variance of the
discrimination rate, sensitivity, or specificity obtained by
repeating verification of the candidate multivariate
discriminant.
[0217] Returning to the description of the multivariate
discriminant-preparing processing, a combination of the amino acid
concentration data contained in the IGT state information used in
preparing the candidate multivariate discriminant is selected by
selecting the explanatory variable of the candidate multivariate
discriminant in the control device based on a predetermined
explanatory variable-selecting method from the verification result
obtained in the step 2 (step 3). The selection of the amino acid
explanatory variable is performed on each candidate multivariate
discriminant prepared in the step 1. In this way, it is possible to
select the amino acid explanatory variable of the candidate
multivariate discriminant properly. The step 1 is executed once
again by using the IGT state information including the amino acid
concentration data selected in the step 3.
[0218] In the step 3, the amino acid explanatory variable of the
candidate multivariate discriminant may be selected based on at
least one of the stepwise method, best path method, local search
method, and genetic algorithm from the verification result obtained
in the step 2.
[0219] The best path method is a method of selecting an amino acid
explanatory variable by optimizing an evaluation index of the
candidate multivariate discriminant while eliminating the amino
acid explanatory variables contained in the candidate multivariate
discriminant one by one.
[0220] Returning to the description of the multivariate
discriminant-preparing processing, the steps 1, 2 and 3 are
repeatedly performed in the control device, and based on
verification results thus accumulated, the candidate multivariate
discriminant used as the multivariate discriminant is selected from
a plurality of the candidate multivariate discriminants, thereby
preparing the multivariate discriminant (step 4). In the selection
of the candidate multivariate discriminants, there are cases where
the optimum multivariate discriminant is selected from the
candidate multivariate discriminants prepared in the same
discriminant-preparing method or the optimum multivariate
discriminant is selected from all candidate multivariate
discriminants.
[0221] As described above, in the multivariate
discriminant-preparing processing, the processing for the
preparation of the candidate multivariate discriminants, the
verification of the candidate multivariate discriminants, and the
selection of the explanatory variables in the candidate
multivariate discriminants are performed based on the IGT state
information in a series of operations in a systematized manner,
whereby the optimum multivariate discriminant for the evaluation of
the IGT state can be prepared. In other words, in the multivariate
discriminant-preparing processing, the amino acid concentration is
used in multivariate statistical analysis, and for selecting the
optimum and robust combination of the explanatory variables, the
explanatory variable-selecting method is combined with
cross-validation to extract the multivariate discriminant having
high diagnosis performance. Logistic regression equation, linear
discriminant, discriminant prepared by support vector machine,
discriminant prepared by Mahalanobis' generalized distance method,
equation prepared by multiple regression analysis, discriminant
prepared by cluster analysis, and the like can be used in the
multivariate discriminant.
2-2. System Configuration
[0222] Hereinafter, a configuration of the IGT-evaluating system
according to the second embodiment (hereinafter referred to
sometimes as the present system) will be described with reference
to FIGS. 4 to 20. This system is merely one example, and the
present invention is not limited thereto.
[0223] First, an entire configuration of the present system will be
described with reference to FIGS. 4 and 5. FIG. 4 is a diagram
showing an example of the entire configuration of the present
system. FIG. 5 is a diagram showing another example of the entire
configuration of the present system. As shown in FIG. 4, the
present system is constituted in which the IGT-evaluating apparatus
100 that evaluates the IGT state in the subject, and the client
apparatus 200 (corresponding to the information communication
terminal apparatus of the present invention) that provides the
amino acid concentration data of the subject on the concentration
values of the amino acids, are communicatively connected to each
other via a network 300.
[0224] In the present system as shown in FIG. 5, in addition to the
IGT-evaluating apparatus 100 and the client apparatus 200, the
database apparatus 400 storing, for example, the IGT state
information used in preparing the multivariate discriminant and the
multivariate discriminant used in evaluating the IGT state in the
IGT-evaluating apparatus 100, may be communicatively connected via
the network 300. In this configuration, the information on the IGT
state etc. are provided via the network 300 from the IGT-evaluating
apparatus 100 to the client apparatuses 200 and the database
apparatus 400, or from the client apparatuses 200 and the database
apparatus 400 to the IGT-evaluating apparatus 100. The "information
on the IGT state" is information on measured values of particular
items of the IGT state of organisms including human. The
information on the IGT state is generated in the IGT-evaluating
apparatus 100, client apparatus 200, and other apparatuses (e.g.,
various measuring apparatuses) and stored mainly in the database
apparatus 400.
[0225] Now, a configuration of the IGT-evaluating apparatus 100 in
the present system will be described with reference to FIGS. 6 to
18. FIG. 6 is a block diagram showing an example of the
configuration of the IGT-evaluating apparatus 100 in the present
system, showing conceptually only the region relevant to the
present invention.
[0226] The IGT-evaluating apparatus 100 includes (a) a control
device 102, such as CPU (Central Processing Unit), that integrally
controls the IGT-evaluating apparatus 100, (b) a communication
interface 104 that connects the IGT-evaluating apparatus 100 to the
network 300 communicatively via communication apparatuses such as a
router and wired or wireless communication lines such as a private
line, (c) a memory device 106 that stores various databases,
tables, files and others, and (d) an input/output interface 108
connected to an input device 112 and an output device 114, and
these parts are connected to each other communicatively via any
communication channel. The IGT-evaluating apparatus 100 may be
present together with various analyzers (e.g., an amino acid
analyzer) in a same housing. A typical configuration of
disintegration/integration of the IGT-evaluating apparatus 100 is
not limited to that shown in the figure, and all or a part of it
may be disintegrated or integrated functionally or physically in
any unit, for example, according to various loads applied. For
example, a part of the processing may be performed via CGI (Common
Gateway Interface).
[0227] The memory device 106 is a storage means, and examples
thereof include memory apparatus such as RAM (Random Access Memory)
and ROM (Read Only Memory), a fixed disk drive such as a hard disk,
a flexible disk, an optical disk, and the like. The memory device
106 stores computer programs giving instructions to the CPU for
various processings, together with OS (Operating System). As shown
in the figure, the memory device 106 stores the user information
file 106a, the amino acid concentration data file 106b, the IGT
state information file 106c, the designated IGT state information
file 106d, a multivariate discriminant-related information database
106e, the discriminant value file 106f, and the evaluation result
file 106g.
[0228] The user information file 106a stores user information on
users. FIG. 7 is a chart showing an example of information stored
in the user information file 106a. As shown in FIG. 7, the
information stored in the user information file 106a includes user
ID (identification) for identifying a user uniquely, user password
for authentication of the user, user name, organization ID for
uniquely identifying an organization of the user, department ID for
uniquely identifying a department of the user organization,
department name, and electronic mail address of the user that are
correlated to one another.
[0229] Returning to FIG. 6, the amino acid concentration data file
106b stores the amino acid concentration data on the concentration
values of the amino acids. FIG. 8 is a chart showing an example of
information stored in the amino acid concentration data file 106b.
As shown in FIG. 8, the information stored in the amino acid
concentration data file 106b includes individual number for
uniquely identifying an individual (sample) as a subject to be
evaluated and amino acid concentration data that are correlated to
one another. In FIG. 8, the amino acid concentration data are
assumed to be numerical values, i.e., on a continuous scale, but
the amino acid concentration data may be expressed on a nominal
scale or an ordinal scale. In the case of the nominal or ordinal
scale, any number may be allocated to each state for analysis. The
amino acid concentration data may be combined with other biological
information (e.g., biological metabolites such as glucose, lipid,
protein, peptide, mineral and hormone, and biological indices such
as blood glucose level, blood pressure level, sex, age, hepatic
disease index, dietary habit, drinking habit, exercise habit,
obesity level and disease history).
[0230] Returning to FIG. 6, the IGT state information file 106c
stores the IGT state information used in preparing the multivariate
discriminant. FIG. 9 is a chart showing an example of information
stored in the IGT state information file 106c. As shown in FIG. 9,
the information stored in the IGT state information file 106c
includes individual number, IGT state index data (T) corresponding
to an IGT state index (index T.sub.1, index T.sub.2, index T.sub.3
. . . ), and amino acid concentration data that are correlated to
one another. In FIG. 9, the IGT state index data and the amino acid
concentration data are assumed to be numerical values, i.e., on a
continuous scale, but the IGT state index data and the amino acid
concentration data may be expressed on a nominal scale or an
ordinal scale. In the case of nominal or ordinal scale, any number
may be allocated to each state for analysis. The IGT state index
data is a single known condition index serving as a marker of the
IGT state, and numerical data may be used.
[0231] Returning to FIG. 6, the designated IGT state information
file 106d stores the IGT state information designated in an IGT
state information-designating part 102g described below. FIG. 10 is
a chart showing an example of information stored in the designated
IGT state information file 106d. As shown in FIG. 10, the
information stored in the designated IGT state information file
106d includes individual number, designated IGT state index data,
and designated amino acid concentration data that are correlated to
one another.
[0232] Returning to FIG. 6, the multivariate discriminant-related
information database 106e is composed of (i) the candidate
multivariate discriminant file 106e1 storing the candidate
multivariate discriminants prepared in a candidate multivariate
discriminant-preparing part 102h1 described below, (ii) the
verification result file 106e2 storing the verification results
obtained in a candidate multivariate discriminant-verifying part
102h2 described below, (iii) the selected IGT state information
file 106e3 storing the IGT state information containing the
combination of the amino acid concentration data selected in an
explanatory variable-selecting part 102h3 described below, and (iv)
the multivariate discriminant file 106e4 storing the multivariate
discriminants prepared in the multivariate discriminant-preparing
part 102h described below.
[0233] The candidate multivariate discriminant file 106e1 stores
the candidate multivariate discriminants prepared in the candidate
multivariate discriminant-preparing part 102h1 described below.
FIG. 11 is a chart showing an example of information stored in the
candidate multivariate discriminant file 106e1. As shown in FIG.
11, the information stored in the candidate multivariate
discriminant file 106e1 includes rank, and candidate multivariate
discriminant (e.g., F.sub.1 (Gly, Leu, Phe, . . . ), F.sub.2 (Gly,
Leu, Phe, . . . ), or F.sub.3 (Gly, Leu, Phe, . . . ) in FIG. 11)
that are correlated to each other.
[0234] Returning to FIG. 6, the verification result file 106e2
stores the verification results obtained in the candidate
multivariate discriminant-verifying part 102h2 described below.
FIG. 12 is a chart showing an example of information stored in the
verification result file 106e2. As shown in FIG. 12, the
information stored in the verification result file 106e2 includes
rank, candidate multivariate discriminant (e.g., F.sub.k (Gly, Leu,
Phe, . . . ), F.sub.m (Gly, Leu, Phe, . . . ), F.sub.1 (Gly, Leu,
Phe, . . . ) in FIG. 12), and verification result of each candidate
multivariate discriminant (e.g., evaluation value of each candidate
multivariate discriminant) that are correlated to one another.
[0235] Returning to FIG. 6, the selected IGT state information file
106e3 stores the IGT state information including the combination of
the amino acid concentration data corresponding to the explanatory
variables selected in the explanatory variable-selecting part 102h3
described below. FIG. 13 is a chart showing an example of
information stored in the selected IGT state information file
106e3. As shown in FIG. 13, the information stored in the selected
IGT state information file 106e3 includes individual number, IGT
state index data designated in the IGT state
information-designating part 102g described below, and amino acid
concentration data selected in the explanatory variable-selecting
part 102h3 described below that are correlated to one another.
[0236] Returning to FIG. 6, the multivariate discriminant file
106e4 stores the multivariate discriminants prepared in the
multivariate discriminant-preparing part 102h described below. FIG.
14 is a chart showing an example of information stored in the
multivariate discriminant file 106e4. As shown in FIG. 14, the
information stored in the multivariate discriminant file 106e4
includes rank, multivariate discriminant (e.g., F.sub.p (Phe, . . .
), F.sub.p (Gly, Leu, Phe), F.sub.k (Gly, Leu, Phe, . . . ) in FIG.
14), threshold corresponding to each discriminant-preparing method,
and verification result of each multivariate discriminant (e.g.,
evaluation value of each multivariate discriminant) that are
correlated to one another.
[0237] Returning to FIG. 6, the discriminant value file 106f stores
the discriminant values calculated in a discriminant
value-calculating part 102i described below. FIG. 15 is a chart
showing an example of information stored in the discriminant value
file 106f. As shown in FIG. 15, the information stored in the
discriminant value file 106f includes individual number for
uniquely identifying the individual (sample) as the subject, rank
(number for uniquely identifying the multivariate discriminant),
and discriminant value that are correlated to one another.
[0238] Returning to FIG. 6, the evaluation result file 106g stores
the evaluation results obtained in the discriminant value
criterion-evaluating part 102j described below (specifically the
discrimination results obtained in a discriminant value
criterion-discriminating part 102j1 described below). FIG. 16 is a
chart showing an example of information stored in the evaluation
result file 106g. The information stored in the evaluation result
file 106g includes individual number for uniquely identifying the
individual (sample) as the subject, previously obtained amino acid
concentration data of the subject, discriminant value calculated in
the multivariate discriminant, and evaluation result on the IGT
state (specifically, discrimination result on the discrimination
between the IGT and the NGT) that are correlated to one
another.
[0239] Returning to FIG. 6, the memory device 106 stores various
Web data for providing the client apparatuses 200 with web site
information, CGI programs, and others as information other than the
information described above. The Web data include data for
displaying various Web pages described below and others, and the
data are generated as, for example, HTML (HyperText Markup
Language) or XML (Extensible Markup Language) text files. Files for
components and files for operation for generation of the Web data,
other temporary files, and the like are also stored in the memory
device 106. In addition, the memory device 106 may store as needed
sound files of sounds for transmission to the client apparatuses
200 in WAVE format or AIFF (Audio Interchange File Format) format
and image files of still images or motion pictures in JPEG (Joint
Photographic Experts Group) format or MPEG2 (Moving Picture Experts
Group phase 2) format.
[0240] The communication interface 104 allows communication between
the IGT-evaluating apparatus 100 and the network 300 (or a
communication apparatus such as a router). Thus, the communication
interface 104 has a function to communicate data via a
communication line with other terminals.
[0241] The input/output interface 108 is connected to the input
device 112 and the output device 114. A monitor (including a home
television), a speaker, or a printer may be used as the output
device 114 (hereinafter, the output device 114 may be described as
a monitor 114). A keyboard, a mouse, a microphone, or a monitor
functioning as a pointing device together with a mouse may be used
as the input device 112.
[0242] The control device 102 has an internal memory storing
control programs such as OS (Operating System), programs for
various processing procedures, and other needed data, and performs
various information processings according to these programs. As
shown in the figure, the control device 102 includes mainly a
request-interpreting part 102a, a browsing processing part 102b, an
authentication-processing part 102c, an electronic mail-generating
part 102d, a Web page-generating part 102e, a receiving part 102f,
the IGT state information-designating part 102g, the multivariate
discriminant-preparing part 102h, the discriminant
value-calculating part 102i, the discriminant value
criterion-evaluating part 102j, a result outputting part 102k, and
a sending part 102m. The control device 102 performs data
processings such as removal of data including defective, removal of
data including many outliers, and removal of explanatory variables
for the defective-including data in the IGT state information
transmitted from the database apparatus 400 and in the amino acid
concentration data transmitted from the client apparatus 200.
[0243] The request-interpreting part 102a interprets requests
transmitted from the client apparatus 200 or the database apparatus
400 and sends the requests to other parts in the control device 102
according to results of interpreting the requests. Upon receiving
browsing requests for various screens transmitted from the client
apparatus 200, the browsing processing part 102b generates and
transmits Web data for these screens. Upon receiving authentication
requests transmitted from the client apparatus 200 or the database
apparatus 400, the authentication-processing part 102c performs
authentication. The electronic mail-generating part 102d generates
electronic mails including various kinds of information. The Web
page-generating part 102e generates Web pages for users to browse
with the client apparatus 200.
[0244] The receiving part 102f receives, via the network 300,
information (specifically, the amino acid concentration data, the
IGT state information, the multivariate discriminant etc.)
transmitted from the client apparatus 200 or the database apparatus
400. The IGT state information-designating part 102g designates
objective IGT state index data and objective amino acid
concentration data in preparing the multivariate discriminant.
[0245] The multivariate discriminant-preparing part 102h generates
the multivariate discriminants based on the IGT state information
received in the receiving part 102f and the IGT state information
designated in the IGT state information-designating part 102g.
Specifically, the multivariate discriminant-preparing part 102h
prepares the multivariate discriminant by selecting the candidate
multivariate discriminant to be used as the multivariate
discriminant from a plurality of the candidate multivariate
discriminants, based on verification results accumulated by
repeating processings in the candidate multivariate
discriminant-preparing part 102h1, the candidate multivariate
discriminant-verifying part 102h2, and the explanatory
variable-selecting part 102h3 from the IGT state information.
[0246] If the multivariate discriminant is stored previously in a
predetermined region of the memory device 106, the multivariate
discriminant-preparing part 102h may prepares the multivariate
discriminant by selecting the desired multivariate discriminant out
of the memory device 106. Alternatively, the multivariate
discriminant-preparing part 102h may prepare the multivariate
discriminant by selecting and downloading the desired multivariate
discriminant from the multivariate discriminants previously stored
in another computer apparatus (e.g., the database apparatus
400).
[0247] Hereinafter, a configuration of the multivariate
discriminant-preparing part 102h will be described with reference
to FIG. 17. FIG. 17 is a block diagram showing the configuration of
the multivariate discriminant-preparing part 102h, and only a part
in the configuration related to the present invention is shown
conceptually. The multivariate discriminant-preparing part 102h has
the candidate multivariate discriminant-preparing part 102h1, the
candidate multivariate discriminant-verifying part 102h2, and the
explanatory variable-selecting part 102h3, additionally. The
candidate multivariate discriminant-preparing part 102h1 prepares
the candidate multivariate discriminant that is a candidate of the
multivariate discriminant, from the IGT state information based on
a predetermined discriminant-preparing method. The candidate
multivariate discriminant-preparing part 102h1 may prepare a
plurality of the candidate multivariate discriminants from the IGT
state information, by using a plurality of the different
discriminant-preparing methods. The candidate multivariate
discriminant-verifying part 102h2 verifies the candidate
multivariate discriminants prepared in the candidate multivariate
discriminant-preparing part 102h1 based on a predetermined
verifying method. The candidate multivariate discriminant-verifying
part 102h2 may verify at least one of the discrimination rate,
sensitivity, specificity, and information criterion of the
candidate multivariate discriminants based on at least one of the
bootstrap method, holdout method, and leave-one-out method. The
explanatory variable-selecting part 102h3 selects the combination
of the amino acid concentration data contained in the IGT state
information used in preparing the candidate multivariate
discriminant, by selecting the explanatory variables of the
candidate multivariate discriminant based on a predetermined
explanatory variable-selecting method from the verification results
obtained in the candidate multivariate discriminant-verifying part
102h2. The explanatory variable-selecting part 102h3 may select the
explanatory variables of the candidate multivariate discriminant
based on at least one of the stepwise method, best path method,
local search method, and genetic algorithm from the verification
results.
[0248] Returning to FIG. 6, the discriminant value-calculating part
102i calculates the discriminant value that is a value of the
multivariate discriminant, based on the amino acid concentration
data (for example, the concentration value of at least one or at
least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp) of the
subject received in the receiving part 102f and the multivariate
discriminant (for example, the multivariate discriminant containing
at least one or at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe
and Asp as the explanatory variable) prepared in the multivariate
discriminant-preparing part 102h.
[0249] The multivariate discriminant may be expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and may contain at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or may contain at least one of Gly and
Ser as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Specifically, the multivariate
discriminant may be formula 1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0250] The multivariate discriminant may be any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Specifically, the
multivariate discriminant may be the logistic regression equation
containing at least Glu and Gly as the explanatory variables.
[0251] The discriminant value criterion-evaluating part 102j
evaluates the IGT state in the subject based on the discriminant
value calculated in the discriminant value-calculating part 102i.
The discriminant value criterion-evaluating part 102j further
includes the discriminant value criterion-discriminating part
102j1. Now, a configuration of the discriminant value
criterion-evaluating part 102j will be described with reference to
FIG. 18. FIG. 18 is a block diagram showing the configuration of
the discriminant value criterion-evaluating part 102j, and only a
part in the configuration related to the present invention is shown
conceptually. The discriminant value criterion-discriminating part
102j1 discriminates between the IGT and the NGT in the subject
based on the discriminant value. Specifically, the discriminant
value criterion-discriminating part 102j1 compares the discriminant
value with a previously established threshold (cutoff value),
thereby discriminating between the IGT and the NGT in the
subject.
[0252] Returning to FIG. 6, the result outputting part 102k
outputs, into the output device 114, the processing results in each
processing part in the control device 102 (the evaluation results
obtained in the discriminant value criterion-evaluating part 102j
(specifically the discrimination results obtained in the
discriminant value criterion-discriminating part 102j1)) etc.
[0253] The sending part 102m transmits the evaluation results to
the client apparatus 200 that is a sender of the amino acid
concentration data of the subject, and transmits the multivariate
discriminants prepared in the IGT-evaluating apparatus 100 and the
evaluation results to the database apparatus 400.
[0254] Hereinafter, a configuration of the client apparatus 200 in
the present system will be described with reference to FIG. 19.
FIG. 19 is a block diagram showing an example of the configuration
of the client apparatus 200 in the present system, and only the
part in the configuration relevant to the present invention is
shown conceptually.
[0255] The client apparatus 200 includes a control device 210, ROM
220, HD (Hard Disk) 230, RAM 240, an input device 250, an output
device 260, an input/output IF 270, and a communication IF 280 that
are connected communicatively to one another through a
communication channel.
[0256] The control device 210 has a Web browser 211, an electronic
mailer 212, a receiving part 213, and a sending part 214. The Web
browser 211 performs browsing processings of interpreting Web data
and displaying the interpreted Web data on a monitor 261 described
below. The Web browser 211 may have various plug-in softwares such
as stream player having functions to receive, display and feedback
streaming screen images. The electronic mailer 212 sends and
receives electronic mails using a particular protocol (e.g., SMTP
(Simple Mail Transfer Protocol) or POPS (Post Office Protocol
version 3)). The receiving part 213 receives various kinds of
information such as the evaluation results transmitted from the
IGT-evaluating apparatus 100, via the communication IF 280. The
sending part 214 sends various kinds of information such as the
amino acid concentration data of the subject, via the communication
IF 280 to the IGT-evaluating apparatus 100.
[0257] The input device 250 is for example a keyboard, a mouse, or
a microphone. The monitor 261 described below also functions as a
pointing device together with a mouse. The output device 260 is an
output means for outputting information received via the
communication IF 280, and includes the monitor 261 (including a
home television) and a printer 262. In addition, the output device
260 may have a speaker or the like additionally. The input/output
IF 270 is connected to the input device 250 and the output device
260.
[0258] The communication IF 280 connects the client apparatus 200
to the network 300 (or communication apparatus such as router)
communicatively. In other words, the client apparatuses 200 are
connected to the network 300 via a communication apparatus such as
a modem, TA (Terminal Adapter) or a router, and a telephone line,
or a private line. In this way, the client apparatuses 200 can
access to the IGT-evaluating apparatus 100 by using a particular
protocol.
[0259] The client apparatus 200 may be realized by installing
softwares (including programs, data and others) for a Web
data-browsing function and an electronic mail-processing function
to an information processing apparatus (for example, an information
processing terminal such as a known personal computer, a
workstation, a family computer, Internet TV (Television), PHS
(Personal Handyphone System) terminal, a mobile phone terminal, a
mobile unit communication terminal or PDA (Personal Digital
Assistants)) connected as needed with peripheral devices such as a
printer, a monitor, and a image scanner.
[0260] All or a part of processings of the control device 210 in
the client apparatus 200 may be performed by CPU and programs read
and executed by the CPU. Computer programs for giving instructions
to the CPU and executing various processings together with OS
(Operating System) are recorded in the ROM 220 or HD 230. The
computer programs, which are executed as they are loaded in the RAM
240, constitute the control device 210 with the CPU. The computer
programs may be stored in application program servers connected via
any network to the client apparatus 200, and the client apparatus
200 may download all or a part of them as needed. All or any part
of processings of the control device 210 may be realized by
hardwares such as wired-logic.
[0261] Hereinafter, the network 300 in the present system will be
described with reference to FIGS. 4 and 5. The network 300 has a
function to connect the IGT-evaluating apparatus 100, the client
apparatuses 200, and the database apparatus 400 mutually,
communicatively to one another, and is for example the Internet, an
intranet, or LAN (Local Area Network (including both
wired/wireless)). The network 300 may be VAN (Value Added Network),
a personal computer communication network, a public telephone
network (including both analog and digital), a leased line network
(including both analog and digital), CATV (Community Antenna
Television) network, a portable switched network or a portable
packet-switched network (including IMT2000 (International Mobile
Telecommunication 2000) system, GSM (Global System for Mobile
Communications) system, PDC (Personal Digital Cellular)/PDC-P
system, and the like), a wireless calling network, a local wireless
network such as Bluetooth (registered trademark), PHS network, a
satellite communication network (including CS (Communication
Satellite), BS (Broadcasting Satellite), ISDB (Integrated Services
Digital Broadcasting), and the like), or the like.
[0262] Hereinafter, a configuration of the database apparatus 400
in the present system will be described with reference to FIG. 20.
FIG. 20 is a block diagram showing an example of the configuration
of the database apparatus 400 in the present system, showing
conceptually only the region relevant to the present invention.
[0263] The database apparatus 400 has functions to store, for
example, the IGT state information used in preparing the
multivariate discriminants in the IGT-evaluating apparatus 100 or
in the database apparatus 400, the multivariate discriminants
prepared in the IGT-evaluating apparatus 100, and the evaluation
results obtained in the IGT-evaluating apparatus 100. As shown in
FIG. 20, the database apparatus 400 includes (a) a control device
402, such as CPU, which integrally controls the entire database
apparatus 400, (b) a communication interface 404 connecting the
database apparatus 400 to the network 300 communicatively via
communication apparatuses such as a router and via wired or
wireless communication circuits such as a private line, (c) a
memory device 406 storing various databases, tables and files (for
example, files for Web pages), and (d) an input/output interface
408 connected to an input device 412 and an output device 414, and
these parts are connected communicatively to each other via any
communication channel.
[0264] The memory device 406 is a storage means, and may be, for
example, memory apparatus such as RAM and ROM, a fixed disk drive
such as a hard disk, a flexible disk, an optical disk, and the
like. The memory device 406 stores various programs used in various
processings. The communication interface 404 allows communication
between the database apparatus 400 and the network 300 (or a
communication apparatus such as a router). Thus, the communication
interface 404 has a function to communicate data via a
communication line with other terminals. The input/output interface
408 is connected to the input device 412 and the output device 414.
A monitor (including a home television), a speaker, or a printer
may be used as the output device 414 (hereinafter, the output
device 414 may be described as a monitor 414). A keyboard, a mouse,
a microphone, or a monitor functioning as a pointing device
together with a mouse may be used as the input device 412.
[0265] The control device 402 has an internal memory storing
control programs such as OS (Operating System), programs for
various processing procedures, and other needed data, and performs
various information processings according to these programs. As
shown in the figure, the control device 402 includes mainly a
request-interpreting part 402a, a browsing processing part 402b, an
authentication-processing part 402c, an electronic mail-generating
part 402d, a Web page-generating part 402e, and a sending part
402f.
[0266] The request-interpreting part 402a interprets requests
transmitted from the IGT-evaluating apparatus 100 and sends the
requests to other parts in the control device 402 according to
results of interpreting the requests. Upon receiving browsing
requests for various screens transmitted from the IGT-evaluating
apparatus 100, the browsing processing part 402b generates and
transmits Web data for these screens. Upon receiving authentication
requests transmitted from the IGT-evaluating apparatus 100, the
authentication-processing part 402c performs authentication. The
electronic mail-generating part 402d generates electronic mails
including various kinds of information. The Web page-generating
part 402e generates Web pages for users to browse with the client
apparatus 200. The sending part 402f transmits various kinds of
information such as the IGT state information and the multivariate
discriminants to the IGT-evaluating apparatus 100.
2-3. Processing in the Present System
[0267] Here, an example of an IGT evaluation service processing
performed in the present system constituted as described above will
be described with reference to FIG. 21. FIG. 21 is a flowchart
showing the example of the IGT evaluation service processing.
[0268] The amino acid concentration data used in the present
processing is data concerning the concentration values of the amino
acids obtained by analyzing blood previously collected from an
individual. Hereinafter, the method of analyzing blood amino acid
will be described briefly. First, a blood sample is collected in a
heparin-treated tube, and then the blood plasma is separated by
centrifugation of the tube. All blood plasma samples separated are
frozen and stored at -70.degree. C. before a measurement of an
amino acid concentration. Before the measurement of the amino acid
concentration, the blood plasma samples are deproteinized by adding
sulfosalicylic acid to a concentration of 3%. An amino acid
analyzer by high-performance liquid chromatography (HPLC) by using
ninhydrin reaction in post column is used for the measurement of
the amino acid concentration.
[0269] First, the client apparatus 200 accesses the IGT-evaluating
apparatus 100 when the user specifies the Web site address (such as
URL) provided from the IGT-evaluating apparatus 100, via the input
device 250 on the screen displaying the Web browser 211.
Specifically, when the user instructs an update of the Web browser
211 screen on the client apparatus 200, the Web browser 211 sends
the Web site address provided from the IGT-evaluating apparatus 100
by a particular protocol to the IGT-evaluating apparatus 100,
thereby transmitting requests demanding a transmission of Web page
corresponding to an amino acid concentration data transmission
screen to the IGT-evaluating apparatus 100 based on a routing of
the address.
[0270] Then, upon receiving the requests transmitted from the
client apparatus 200, the request-interpreting part 102a in the
IGT-evaluating apparatus 100 analyzes the transmitted requests and
sends the requests to other parts in the control device 102
according to analysis results. Specifically, when the transmitted
requests are requests to send the Web page corresponding to the
amino acid concentration data transmission screen, mainly the
browsing processing part 102b in the IGT-evaluating apparatus 100
obtains the Web data for displaying the Web page stored in a
predetermined region of the memory device 106 and sends the
obtained Web data to the client apparatus 200. More specifically,
upon receiving the requests to transmit the Web page corresponding
to the amino acid concentration data transmission screen by the
user, the control device 102 in the IGT-evaluating apparatus 100
demands inputs of user ID and user password from the user. If the
user ID and password are input, the authentication-processing part
102c in the IGT-evaluating apparatus 100 examines the input user ID
and password by comparing them with the user ID and user password
stored in the user information file 106a for authentication. Only
when the user is authenticated, the browsing processing part 102b
in the IGT-evaluating apparatus 100 sends the Web data for
displaying the Web page corresponding to the amino acid
concentration data transmission screen to the client apparatus 200.
The client apparatus 200 is identified with the IP (Internet
Protocol) address transmitted from the client apparatus 200
together with the transmission requests.
[0271] Then, the client apparatus 200 receives, in the receiving
part 213, the Web data (for displaying the Web page corresponding
to the amino acid concentration data transmission screen)
transmitted from the IGT-evaluating apparatus 100, interprets the
received Web data with the Web browser 211, and displays the amino
acid concentration data transmission screen on the monitor 261.
[0272] When the user inputs and selects, via the input device 250,
for example the amino acid concentration data of the individual on
the amino acid concentration data transmission screen displayed on
the monitor 261, the sending part 214 in the client apparatus 200
transmits an identifier for identifying input information and
selected items to the IGT-evaluating apparatus 100, thereby
transmitting the amino acid concentration data of the individual as
the subject to the IGT-evaluating apparatus 100 (step SA-21). In
the step SA-21, the transmission of the amino acid concentration
data may be realized for example by using an existing file transfer
technology such as FTP (File Transfer Protocol).
[0273] Then, the request-interpreting part 102a in the
IGT-evaluating apparatus 100 interprets the identifier transmitted
from the client apparatus 200 thereby interpreting the requests
from the client apparatus 200, and requests the database apparatus
400 to send the multivariate discriminants for an evaluation of the
IGT state (specifically, for a discrimination between the 2 groups
of the IGT and the NGT) containing at least one or at least two of
Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the explanatory
variable.
[0274] Then, the request-interpreting part 402a in the database
apparatus 400 interprets the transmission requests from the
IGT-evaluating apparatus 100 and transmits, to the IGT-evaluating
apparatus 100, the multivariate discriminant (for example, the
updated newest multivariate discriminant) stored in a predetermined
memory region of the memory device 406 containing at least one or
at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp as the
explanatory variable (step SA-22).
[0275] In the step SA-22, the multivariate discriminant transmitted
to the IGT-evaluating apparatus 100 may be expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and may contain at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or may contain at least one of Gly and
Ser as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Specifically, the multivariate
discriminant may be formula 1.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0276] In the step SA-22, the multivariate discriminant transmitted
to the IGT-evaluating apparatus 100 may be any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Specifically, the
multivariate discriminant may be the logistic regression equation
containing at least Glu and Gly as the explanatory variables.
[0277] The IGT-evaluating apparatus 100 receives, in the receiving
part 102f, the amino acid concentration data of the individual
transmitted from the client apparatuses 200 and the multivariate
discriminant transmitted from the database apparatus 400, and
stores the received amino acid concentration data in a
predetermined memory region of the amino acid concentration data
file 106b and the received multivariate discriminant in a
predetermined memory region of the multivariate discriminant file
106e4 (step SA-23).
[0278] Then, the control device 102 in the IGT-evaluating apparatus
100 removes data such as defective and outliers from the amino acid
concentration data of the individual received in the step SA-23
(step SA-24).
[0279] Then, the discriminant value-calculating part 102i in the
IGT-evaluating apparatus 100 calculates the discriminant value
based on both the multivariate discriminant received in the step
SA-23 and the concentration value of at least one or at least two
of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the amino
acid concentration data of the individual from which the data such
as the defective and outliers have been removed in the step SA-24
(step SA-25).
[0280] Then, the discriminant value criterion-discriminating part
102j1 in the IGT-evaluating apparatus 100 compares the discriminant
value calculated in the step SA-25 with a previously established
threshold (cutoff value), thereby discriminating between the IGT
and the NGT in the individual, and stores the discrimination
results in a predetermined memory region of the evaluation result
file 106g (step SA-26).
[0281] Then, the sending part 102m in the IGT-evaluating apparatus
100 sends, to the client apparatus 200 that has sent the amino acid
concentration data and to the database apparatus 400, the
discrimination results (the discrimination results on the
discrimination between the IGT and the NGT) obtained in the step
SA-26 (step SA-27). Specifically, the IGT-evaluating apparatus 100
first generates a Web page for displaying the discrimination
results in the Web page-generating part 102e and stores the Web
data corresponding to the generated Web page in a predetermined
memory region of the memory device 106. Then, the user is
authenticated as described above by inputting a predetermined URL
(Uniform Resource Locator) into the Web browser 211 of the client
apparatus 200 via the input device 250, and the client apparatus
200 sends a Web page browsing request to the IGT-evaluating
apparatus 100. The IGT-evaluating apparatus 100 then interprets the
browsing request transmitted from the client apparatus 200 in the
browsing processing part 102b and reads the Web data corresponding
to, the Web page for displaying the discrimination results, out of
the predetermined memory region of the memory device 106. The
sending part 102m in the IGT-evaluating apparatus 100 then sends
the read-out Web data to the client apparatus 200 and
simultaneously sends the Web data or the discrimination results to
the database apparatus 400.
[0282] In the step SA-27, the control device 102 in the
IGT-evaluating apparatus 100 may notify the discrimination results
to the user client apparatus 200 by electronic mail. Specifically,
the electronic mail-generating part 102d in the IGT-evaluating
apparatus 100 first acquires the user electronic mail address by
referencing the user information stored in the user information
file 106a based on the user ID and the like at the transmission
timing. The electronic mail-generating part 102d in the
IGT-evaluating apparatus 100 then generates electronic mail data
with the acquired electronic mail address as its mail address,
including the user name and the discrimination results. The sending
part 102m in the IGT-evaluating apparatus 100 then transmits the
generated electronic mail data to the user client apparatus
200.
[0283] Also in the step SA-27, the IGT-evaluating apparatus 100 may
send the discrimination results to the user client apparatus 200 by
using, for example, an existing file transfer technology such as
FTP.
[0284] Returning to FIG. 21, the control device 402 in the database
apparatus 400 receives the discrimination results or the Web data
transmitted from the IGT-evaluating apparatus 100 and stores
(accumulates) the received discrimination results or the received
Web data in a predetermined memory region of the memory device 406
(step SA-28).
[0285] The receiving part 213 in the client apparatus 200 receives
the Web data transmitted from the IGT-evaluating apparatus 100, and
the received Web data is interpreted with the Web browser 211, to
display on the monitor 261 the Web page screen displaying the
discrimination results of the individual (step SA-29). When the
discrimination results are sent from the IGT-evaluating apparatus
100 by electronic mail, the electronic mail transmitted from the
IGT-evaluating apparatus 100 is received at any timing, and the
received electronic mail is displayed on the monitor 261 with the
known function of the electronic mailer 212 in the client apparatus
200.
[0286] In this way, the user can confirm the discrimination results
on the discrimination of the 2 groups of the IGT and the NGT in the
individual, by browsing the Web page displayed on the monitor 261.
The user can print out the contents of the Web page displayed on
the monitor 261 by the printer 262.
[0287] When the discrimination results are transmitted by
electronic mail from the IGT-evaluating apparatus 100, the user
reads the electronic mail displayed on the monitor 261, whereby the
user can confirm the discrimination results on the discrimination
of the 2 groups of the IGT and the NGT in the individual. The user
may print out the contents of the electronic mail displayed on the
monitor 261 by the printer 262.
[0288] Given the foregoing description, the explanation of the IGT
evaluation service processing is finished.
2-4. Summary of the Second Embodiment and Other Embodiments
[0289] According to the IGT-evaluating system described above in
detail, the client apparatus 200 sends the amino acid concentration
data of the individual to the IGT-evaluating apparatus 100. Upon
receiving the requests from the IGT-evaluating apparatus 100, the
database apparatus 400 transmits the multivariate discriminant for
the discrimination of the 2 groups of the IGT and the NGT, to the
IGT-evaluating apparatus 100. By the IGT-evaluating apparatus 100,
(1) the amino acid concentration data transmitted from the client
apparatus 200 is received and the multivariate discriminant
transmitted from the database apparatus 400 is received
simultaneously, (2) the discriminant values are calculated based on
the received amino acid concentration data and the received
multivariate discriminant, (3) the calculated discriminant values
are compared with the previously established threshold, thereby
discriminating between the IGT and the NGT in the individual, and
(4) the discrimination results are transmitted to the client
apparatus 200 and database apparatus 400. Then, the client
apparatus 200 receives and displays the discrimination results
transmitted from the IGT-evaluating apparatus 100, and the database
apparatus 400 receives and stores the discrimination results
transmitted from the IGT-evaluating apparatus 100. Thus, the
discriminant values obtained in the multivariate discriminants
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0290] According to the IGT-evaluating system, the multivariate
discriminant may be expressed by one fractional expression or the
sum of a plurality of the fractional expressions, and may contain
at least one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory
variable in the numerator in the fractional expression constituting
the multivariate discriminant, and at least one of Gly and Ser as
the explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant, or may
contain at least one of Gly and Ser as the explanatory variable in
the numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the denominator in the
fractional expression constituting the multivariate discriminant.
Specifically, the multivariate discriminant may be formula 1. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0291] According to the IGT-evaluating system, the multivariate
discriminant may be any one of a logistic regression equation, a
linear discriminant, a multiple regression equation, a discriminant
prepared by a support vector machine, a discriminant prepared by a
Mahalanobis' generalized distance method, a discriminant prepared
by canonical discriminant analysis, and a discriminant prepared by
a decision tree. Specifically, the multivariate discriminant may be
the logistic regression equation containing at least Glu and Gly as
the explanatory variables. Thus, discriminant values obtained in
multivariate discriminants useful particularly for discriminating
between the 2 groups of the IGT and the NGT can be utilized to
bring about an effect of enabling a more accurate discrimination
between the 2 groups of the IGT and the NGT.
[0292] The multivariate discriminants described above can be
prepared by a method described in International Publication WO
2004/052191 Pamphlet that is an international application filed by
the present applicant or by a method (multivariate
discriminant-preparing processing described later) described in
International Publication WO 2006/098192 Pamphlet that is an
international application filed by the present applicant. Any
multivariate discriminants obtained by these methods can be
preferably used in the evaluation of the IGT state, regardless of
the unit of the amino acid concentration in the amino acid
concentration data as input data.
[0293] In addition to the second embodiment described above, the
IGT-evaluating apparatus, the IGT-evaluating method, the
IGT-evaluating system, the IGT-evaluating program product and the
recording medium according to the present invention can be
practiced in various different embodiments within the technological
scope of the claims. For example, among the processings described
in the second embodiment above, all or a part of the processings
described above as performed automatically may be performed
manually, and all or a part of the manually conducted processings
may be performed automatically by known methods. In addition, the
processing procedure, control procedure, specific name, various
registered data, information including parameters such as retrieval
condition, screen, and database configuration shown in the
description above or drawings may be modified arbitrarily, unless
specified otherwise. For example, the components of the
IGT-evaluating apparatus 100 shown in the figures are conceptual
and functional and may not be the same physically as those shown in
the figure. In addition, all or an arbitrary part of the
operational function of each component and each device in the
IGT-evaluating apparatus 100 (in particular, the operational
functions executed in the control device 102) may be executed by
the CPU (Central Processing Unit) or the programs executed by the
CPU, and may be realized as wired-logic hardware.
[0294] The "program" is a data processing method written in any
language or by any description method and may be of any format such
as source code or binary code. The "program" may be not limited to
a program configured singly, and may include a program configured
decentrally as a plurality of modules or libraries, and a program
to achieve the function together with a different program such as
OS (Operating System). The program is stored on a recording medium
and read mechanically as needed by the IGT-evaluating apparatus
100. Any well-known configuration or procedure may be used as
specific configuration, reading procedure, installation procedure
after reading, and the like for reading the programs recorded on
the recording medium in each apparatus.
[0295] The "recording media" includes any "portable physical
media", "fixed physical media", and "communication media". Examples
of the "portable physical media" include flexible disk, magnetic
optical disk, ROM, EPROM (Erasable Programmable Read Only Memory),
EEPROM (Electronically Erasable and Programmable Read Only Memory),
CD-ROM (Compact Disk Read Only Memory), MO (Magneto-Optical disk),
DVD (Digital Versatile Disk), and the like. Examples of the "fixed
physical media" include ROM, RAM, HD, and the like which are
installed in various computer systems. The "communication media"
for example stores the program for a short period of time such as
communication line and carrier wave when the program is transmitted
via a network such as LAN (Local Area Network), WAN (Wide Area
Network), or the Internet.
[0296] Finally, an example of the multivariate
discriminant-preparing processing performed in the IGT-evaluating
apparatus 100 is described in detail with reference to FIG. 22.
FIG. 22 is a flowchart showing an example of the multivariate
discriminant-preparing processing. The multivariate
discriminant-preparing processing may be performed in the database
apparatus 400 handling the IGT state information.
[0297] In the present description, the IGT-evaluating apparatus 100
stores the IGT state information previously obtained from the
database apparatus 400 in a predetermined memory region of the IGT
state information file 106c. The IGT-evaluating apparatus 100 shall
store, in a predetermined memory region of the designated IGT state
information file 106d, the IGT state information including the IGT
state index data and amino acid concentration data designated
previously in the IGT state information-designating part 102g.
[0298] The candidate multivariate discriminant-preparing part 102h1
in the multivariate discriminant-preparing part 102h first prepares
the candidate multivariate discriminant according to a
predetermined discriminant-preparing method from the IGT state
information stored in a predetermine memory region of the
designated IGT state information file 106d, and stores the prepared
candidate multivariate discriminate in a predetermined memory
region of the candidate multivariate discriminant file 106e1 (step
SB-21). Specifically, the candidate multivariate
discriminant-preparing part 102h1 in the multivariate
discriminant-preparing part 102h first selects a desired method out
of a plurality of different discriminant-preparing methods
(including those for multivariate analysis such as principal
component analysis, discriminant analysis, support vector machine,
multiple regression analysis, logistic regression analysis, k-means
method, cluster analysis, and decision tree) and determines the
form of the candidate multivariate discriminant to be
prepared'based on the selected discriminant-preparing method. The
candidate multivariate discriminant-preparing part 102h1 in the
multivariate discriminant-preparing part 102h then performs various
calculation corresponding to the selected function-selecting method
(e.g., average or variance), based on the IGT state information.
The candidate multivariate discriminant-preparing part 102h1 in the
multivariate discriminant-preparing part 102h then determines the
parameters for the calculation result and the determined candidate
multivariate discriminant. In this way, the candidate multivariate
discriminant is generated based on the selected
discriminant-preparing method. When candidate multivariate
discriminants are generated simultaneously and concurrently (in
parallel) by using a plurality of different discriminant-preparing
methods in combination, the processings described above may be
executed concurrently for each selected discriminant-preparing
method. Alternatively when candidate multivariate discriminants are
generated in series by using a plurality of different
discriminant-preparing methods in combination, for example,
candidate multivariate discriminants may be generated by converting
the IGT state information with the candidate multivariate
discriminant prepared by performing principal component analysis
and performing discriminant analysis of the converted IGT state
information.
[0299] The candidate multivariate discriminant-verifying part 102h2
in the multivariate discriminant-preparing part 102h verifies
(mutually verifies) the candidate multivariate discriminant
prepared in the step SB-21 according to a predetermined verifying
method and stores the verification result in a predetermined memory
region of the verification result file 106e2 (step SB-22).
Specifically, the candidate multivariate discriminant-verifying
part 102h2 in the multivariate discriminant-preparing part 102h
first generates the verification data to be used in verification of
the candidate multivariate discriminant, based on the IGT state
information stored in a predetermined memory region of the
designated IGT state information file 106d, and verifies the
candidate multivariate discriminant according to the generated
verification data. If a plurality of candidate multivariate
discriminants are generated by using a plurality of different
discriminant-preparing methods in the step SB-21, the candidate
multivariate discriminant-verifying part 102h2 in the multivariate
discriminant-preparing part 102h verifies each candidate
multivariate discriminant corresponding to each
discriminant-preparing method according to a predetermined
verifying method. Here in the step SB-22, at least one of the
discrimination rate, sensitivity, specificity, information
criterion, and the like of the candidate multivariate discriminant
may be verified based on at least one of the bootstrap method,
holdout method, leave-one-out method, and the like. Thus, it is
possible to select the candidate multivariate discriminant higher
in predictability or reliability, by taking the IGT state
information and the diagnostic condition into consideration.
[0300] Then, the explanatory variable-selecting part 102h3 in the
multivariate discriminant-preparing part 102h selects the
combination of the amino acid concentration data contained in the
IGT state information used in preparing the candidate multivariate
discriminant by selecting the explanatory variable of the candidate
multivariate discriminant from the verification result obtained in
the step SB-22 according to a predetermined explanatory
variable-selecting method, and stores the IGT state information
including the selected combination of the amino acid concentration
data in a predetermined memory region of the selected IGT state
information file 106e3 (step SB-23). When a plurality of candidate
multivariate discriminants are generated by using a plurality of
different discriminant-preparing methods in the step SB-21 and each
candidate multivariate discriminant corresponding to each
discriminant-preparing method is verified according to a
predetermined verifying method in the step SB-22, the explanatory
variable-selecting part 102h3 in the multivariate
discriminant-preparing part 102h selects the explanatory variable
of the candidate multivariate discriminant for each candidate
multivariate discriminant corresponding to the verification result
obtained in the step SB-22, according to a predetermined
explanatory variable-selecting method in the step SB-23. Here in
the step SB-23, the explanatory variable of the candidate
multivariate discriminant may be selected from the verification
results according to at least one of the stepwise method, best path
method, local search method, and genetic algorithm. The best path
method is a method of selecting an explanatory variable by
optimizing an evaluation index of the candidate multivariate
discriminant while eliminating the explanatory variables contained
in the candidate multivariate discriminant one by one. In the step
SB-23, the explanatory variable-selecting part 102h3 in the
multivariate discriminant-preparing part 102h may select the
combination of the amino acid concentration data based on the IGT
state information stored in a predetermined memory region of the
designated IGT state information file 106d.
[0301] The multivariate discriminant-preparing part 102h then
judges whether all combinations of the amino acid concentration
data contained in the IGT state information stored in a
predetermined memory region of the designated IGT state information
file 106d are processed, and if the judgment result is "End" (Yes
in step SB-24), the processing advances to the next step (step
SB-25), and if the judgment result is not "End" (No in step SB-24),
it returns to the step SB-21. The multivariate
discriminant-preparing part 102h judges whether the processing is
performed a predetermined number of times, and if the judgment
result is "End" (Yes in step SB-24), the processing may advance to
the next step (step SB-25), and if the judgment result is not "End"
(No in step SB-24), it may return to the step SB-21. The
multivariate discriminant-preparing part 102h may judge whether the
combination of the amino acid concentration data selected in the
step SB-23 is the same as the combination of the amino acid
concentration data contained in the IGT state information stored in
a predetermined memory region of the designated IGT state
information file 106d or the combination of the amino acid
concentration data selected in the previous step SB-23, and if the
judgment result is "the same" (Yes in step SB-24), the processing
may advance to the next step (step SB-25) and if the judgment
result is not "the same" (No in step SB-24), it may return to step
SB-21. If the verification result is specifically the evaluation
value for each multivariate discriminant, the multivariate
discriminant-preparing part 102h may advance to the step SB-25 or
return to the step SB-21, based on the comparison of the evaluation
value with a particular threshold corresponding to each
discriminant-preparing method.
[0302] Then, the multivariate discriminant-preparing part 102h
determines the multivariate discriminant by selecting the candidate
multivariate discriminant used as the multivariate discriminant
based on the verification results from a plurality of the candidate
multivariate discriminants, and stores the determined multivariate
discriminant (the selected candidate multivariate discriminant) in
particular memory region of the multivariate discriminant file
106e4 (step SB-25). Here, in the step SB-25, for example, there are
cases where the optimal multivariate discriminant is selected from
the candidate multivariate discriminants prepared in the same
discriminant-preparing method or the optimal multivariate
discriminant is selected from all candidate multivariate
discriminants.
[0303] Given the foregoing description, the explanation of the
multivariate discriminant-preparing processing is finished.
Third Embodiment
3-1. Outline of the Invention
[0304] Herein, the method of searching for
prophylactic/ameliorating substance for IGT of the present
invention is described in detail with reference to FIG. 23. FIG. 23
is a principle configurational diagram showing a basic principle of
the present invention.
[0305] First, a desired substance group consisting of one or more
substances is administered to a subject to be evaluated (for
example, an individual such as an animal or a human) (step S-31).
For example, a suitable combination of an existing drug, amino
acid, food and supplement capable of administration to humans (for
example, a suitable combination of a drug, supplement and
anti-obesity drug that are known to be effective in amelioration of
various symptoms of IGT) may be administered over a predetermined
period (for example in the range of 1 day to 12 months) in a
predetermined amount at predetermined frequency and timing (for
example 3 times per day, after food) by a predetermined
administration method (for example, oral administration). The
administration method, dose, and dosage form may be suitably
combined depending on the condition of a patient. The dosage form
may be determined based on known techniques. The dose is not
particularly limited, and for example, a drug containing 1 .mu.g to
100g active ingredient may be given.
[0306] From the subject administered with the substance group in
the step S-31, blood is then collected (step S-32).
[0307] Amino acid concentration data on concentration values of
amino acids are measured from the blood collected in the step S-32
(step S-33). The concentrations of amino acids in blood may be
analyzed in the following manner. A blood sample is collected in a
heparin-treated tube, and then the blood plasma is separated by
centrifugation of the collected blood sample. All blood plasma
samples separated are frozen and stored at -70.degree. C. before a
measurement of an amino acid concentration. Before the measurement
of the amino acid concentration, the blood plasma samples are
defrosted, and the defrosted blood plasma samples are deproteinized
by adding sulfosalicylic acid to a concentration of 3%. The
concentration values of various amino acids are measured by
analyzing the deproteinized blood plasma samples by an amino acid
analyzer by high-performance liquid chromatography (HPLC) by using
ninhydrin reaction in post column.
[0308] Then, an IGT state in the subject is evaluated based on the
amino acid concentration data of the subject measured in the step
S-33 (step S-34).
[0309] Then, whether or not the substance group administered in the
step S-31 prevents the IGT or ameliorates the IGT state is judged
based on an evaluation result in the step S-34 (step S-35).
[0310] When a judgment result in the step S-35 is "preventive or
ameliorative", the substance group administered in the step S-31 is
searched as one preventing the IGT or ameliorating the IGT
state.
[0311] According to the present invention, (1) the desired
substance group is administered to the subject, (2) blood is
collected from the subject to which the desired substance group has
been administered, (3) the amino acid concentration data on the
concentration values of the amino acids is measured, (4) the IGT
state in the subject is evaluated based on the measured amino acid
concentration data, and (5) it is judged whether or not the desired
substance group prevents the IGT or ameliorates the IGT state based
on the evaluation results. Thus, the method of evaluating IGT
capable of accurately evaluating the IGT state by utilizing the
concentrations of the amino acids in blood can be used to bring
about an effect of enabling an accurate search for substances for
preventing the IGT or ameliorating the IGT state.
[0312] Before the step S-34 is executed, data such as defective and
outliers may be removed from the amino acid concentration data.
Thereby, the IGT state can be more accurately evaluated.
[0313] In the step S-34, the IGT state in the subject may be
evaluated based on the concentration value of at least one of Glu,
Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject measured in the step S-33. Thus,
the concentrations of the amino acids which among amino acids in
blood, are associated with the IGT state can be utilized to bring
about an effect of enabling an accurate evaluation of the IGT
state.
[0314] In the step S-34, a discrimination between the IGT and a NGT
in the subject may be conducted based on the concentration value of
at least one of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained
in the amino acid concentration data of the subject measured in the
step S-33. Specifically, the concentration value of at least one of
Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp may be compared with a
previously established threshold (cutoff value), thereby
discriminating between the IGT and the NGT in the subject. Thus,
the concentrations of the amino acids which among amino acids in
blood, are useful for discriminating between the 2 groups of the
IGT and the NGT can be utilized to bring about an effect of
enabling an accurate discrimination between the 2 groups of the IGT
and the NGT.
[0315] In the step S-34, the discrimination between the IGT and the
NGT in the subject may be conducted based on the concentration
values of at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the subject
measured in the step S-33. Specifically, the concentration values
of at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp may be
compared with a previously established threshold (cutoff value),
thereby discriminating between the IGT and the NGT in the subject.
Thus, the concentrations of the amino acids which among amino acids
in blood, are useful for discriminating between the 2 groups of the
IGT and the NGT can be utilized to bring about an effect of
enabling an accurate discrimination between the 2 groups of the IGT
and the NGT.
[0316] In the step S-34, a discriminant value that is a value of a
multivariate discriminant with a concentration of the amino acid as
an explanatory variable may be calculated based on both the amino
acid concentration data of the subject measured in the step S-33
and the previously established multivariate discriminant, and the
IGT state in the subject may be evaluated based on the calculated
discriminant value. Thus, the discriminant values obtained in the
multivariate discriminants with the concentrations of the amino
acids as the explanatory variables can be utilized to bring about
an effect of enabling an accurate evaluation of the IGT state.
[0317] In the step S-34, the discriminant value may be calculated
based on both the concentration value of at least one of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject measured in the step S-33 and the
multivariate discriminant containing at least one of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variable, and the IGT
state in the subject may be evaluated based on the calculated
discriminant value. Thus, the discriminant values obtained in the
multivariate discriminants which are correlated with the IGT state
significantly can be utilized to bring about an effect of enabling
an accurate evaluation of the IGT state.
[0318] In the step S-34, the discrimination between the IGT and the
NGT in the subject may be conducted based on the calculated
discriminant value. Specifically, the discriminant value may be
compared with a previously established threshold (cutoff value),
thereby discriminating between the IGT and the NGT in the subject.
Thus, the discriminant values obtained in the multivariate
discriminants useful for discriminating between the 2 groups of the
IGT and the NGT can be utilized to bring about an effect of
enabling an accurate discrimination between the 2 groups of the IGT
and the NGT.
[0319] In the step S-34, the discriminant value may be calculated
based on both the concentration values of at least two of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the subject measured in the step S-33 and the
multivariate discriminant containing at least two of Glu, Gly, Ser,
Ile, Val, Leu, Phe and Asp as the explanatory variables, and the
discrimination between the IGT and the NGT in the subject may be
conducted based on the calculated discriminant value. Thus, the
discriminant values obtained in the multivariate discriminants
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0320] The multivariate discriminant may be expressed by one
fractional expression or the sum of a plurality of the fractional
expressions, and may contain at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the numerator in the
fractional expression constituting the multivariate discriminant,
and at least one of Gly and Ser as the explanatory variable in the
denominator in the fractional expression constituting the
multivariate discriminant, or may contain at least one of Gly and
Ser as the explanatory variable in the numerator in the fractional
expression constituting the multivariate discriminant, and at least
one of Glu, Ile, Val, Leu, Phe and Asp as the explanatory variable
in the denominator in the fractional expression constituting the
multivariate discriminant. Specifically, the multivariate
discriminant may be formula 1. Thus, discriminant values obtained
in multivariate discriminants useful particularly for
discriminating between the 2 groups of the IGT and the NGT can be
utilized to bring about an effect of enabling a more accurate
discrimination between the 2 groups of the IGT and the NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0321] The multivariate discriminant may be any one of a logistic
regression equation, a linear discriminant, a multiple regression
equation, a discriminant prepared by a support vector machine, a
discriminant prepared by a Mahalanobis' generalized distance
method, a discriminant prepared by canonical discriminant analysis,
and a discriminant prepared by a decision tree. Specifically, the
multivariate discriminant may be the logistic regression equation
containing at least Glu and Gly as the explanatory variables. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0322] The multivariate discriminants described above can be
prepared by a method described in International Publication WO
2004/052191 Pamphlet that is an international application filed by
the present applicant or by a method (multivariate
discriminant-preparing processing described in the second
embodiment described above) described in International Publication
WO 2006/098192 Pamphlet that is an international application filed
by the present applicant. Any multivariate discriminants obtained
by these methods can be preferably used in the evaluation of the
IGT state, regardless of the unit of the amino acid concentration
in the amino acid concentration data as input data.
[0323] In the fractional expression, the numerator of the
fractional expression is expressed by the sum of the amino acids A,
B, C etc. and the denominator of the fractional expression is
expressed by the sum of the amino acids a, b, c etc. The fractional
expression also includes the sum of the fractional expressions
.alpha., .beta., .gamma. etc. (for example, .alpha.+.beta.) having
such constitution. The fractional expression also includes divided
fractional expressions. The amino acids used in the numerator or
denominator may have suitable coefficients respectively. The amino
acids used in the numerator or denominator may appear repeatedly.
Each fractional expression may have a suitable coefficient. A value
of a coefficient for each explanatory variable and a value for a
constant term may be any real numbers. In combinations where
explanatory variables in the numerator and explanatory variables in
the denominator in the fractional expression are switched with each
other, the positive (or negative) sign is generally reversed in
correlation with objective explanatory variables, but because their
correlation is maintained, such combinations can be assumed to be
equivalent to one another in discrimination, and thus the
fractional expression also includes combinations where explanatory
variables in the numerator and explanatory variables in the
denominator in the fractional expression are switched with each
other.
[0324] The multivariate discriminant refers to a form of equation
used generally in multivariate analysis and includes, for example,
multiple regression equation, multiple logistic regression
equation, linear discriminant function, Mahalanobis' generalized
distance, canonical discriminant function, support vector machine,
and decision tree. The multivariate discriminant also includes an
equation shown by the sum of different forms of multivariate
discriminants. In the multiple regression equation, multiple
logistic regression equation and canonical discriminant function, a
coefficient and constant term are added to each explanatory
variable, and the coefficient and constant term in this case are
preferably real numbers, more preferably values in the range of 99%
confidence interval for the coefficient and constant term obtained
from data for discrimination, more preferably in the range of 95%
confidence interval for the coefficient and constant term obtained
from data for discrimination. The value of each coefficient and the
confidence interval thereof may be those multiplied by a real
number, and the value of each constant term and the confidence
interval thereof may be those having an arbitrary actual constant
added or subtracted or those multiplied or divided by an arbitrary
actual constant. When an expression such as a logistic regression,
a linear discriminant, and a multiple regression analysis is used
as an index, a linear transformation of the expression (addition of
a constant and multiplication by a constant) and a monotonic
increasing (decreasing) transformation (for example, a logit
transformation) of the expression do not alter discrimination
capability, and thus are equivalent. Therefore, the expression
includes an expression that is subjected to a linear transformation
and a monotonic increasing (decreasing) transformation.
[0325] When the IGT state is evaluated (specifically, the
discrimination between the IGT and the NGT is conducted) in the
present invention, concentrations of other metabolites (biological
metabolites), protein expression level, age and sex of the subject,
biological indices or the like may be used in addition to the
concentrations of the amino acids. When the IGT state is evaluated
(specifically, the discrimination between the IGT and the NGT is
conducted) in the present invention, concentrations of other
metabolites (biological metabolites), protein expression level, age
and sex of the subject, biological indices or the like may be used
as the explanatory variables in the multivariate discriminants in
addition to the concentrations of the amino acids.
3-2. An Example of the Method of Searching for
Prophylactic/Ameliorating Substance for IGT According to the Third
Embodiment
[0326] Here, an example of the method of searching for
prophylactic/ameliorating substance for IGT according to the third
embodiment is described with reference to FIG. 24. FIG. 24 is a
flowchart showing an example of the method of searching for
prophylactic/ameliorating substance for IGT according to the third
embodiment.
[0327] First, a desired substance group consisting of one or more
substances is administered to an individual such as an animal or a
human with the IGT (step SA-31).
[0328] From the individual administered with the substance group in
the step S-31, blood is then collected (step SA-32).
[0329] From the blood collected in the step S-32, the amino acid
concentration data on the concentration values of the amino acids
are measured (step SA-33). The measurement of the concentration
values of the amino acids is conducted by the method described
above.
[0330] From the amino acid concentration data of the individual
measured in the step S-33, data such as defective and outliers is
then removed (step SA-34).
[0331] Then, the concentration value of at least one or at least
two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the
amino acid concentration data of the individual from which the data
such as the defective and outliers was removed in the step SA-34 is
compared with a previously established threshold (cutoff value),
thereby discriminating between the IGT and the NGT in the
individual (step SA-35).
[0332] Based on the discrimination results in the step SA-35, it is
then judged whether or not the substance group administered in the
step SA-31 prevents the IGT or ameliorates the IGT state (step
SA-36).
[0333] When the judgment result obtained in the step SA-36 is
"preventive or ameliorative", the substance group administered in
the step SA-31 is searched as one preventing the IGT or
ameliorating the IGT state. The substances searched by the
searching method of the present embodiment include, for example, an
amino acid group of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp.
3-3. Summary of the Third Embodiment and Other Embodiments
[0334] According to the method of searching for
prophylactic/ameliorating substance for IGT according to the third
embodiment described in detail above, (1) the desired substance
group is administered to the individual, (2) the blood is collected
from the individual administered with the substance group, (3) the
amino acid concentration data are measured from the collected
blood, (4) the data such as the defective and outliers is removed
from the measured amino acid concentration data of the individual,
(5) the concentration value of at least one or at least two of Glu,
Gly, Ser, Ile, Val, Leu, Phe and Asp contained in the amino acid
concentration data of the individual from which the data such as
the defective and outliers was removed is compared with the
previously established threshold (cutoff value), thereby
discriminating between the IGT and the NGT in the individual, and
(6) based on the discrimination result, it is judged whether or not
the administered substance group prevents the IGT or ameliorates
the IGT state. Thus, the method of evaluating IGT of the first
embodiment described above capable of accurately evaluating the IGT
state by utilizing the concentrations of the amino acids in blood
can be used to bring about an effect of enabling an accurate search
for the substance for preventing the IGT or ameliorating the IGT
state.
[0335] In the step SA-35, (a) the discriminant value may be
calculated based on both (i) the concentration value of at least
one or at least two of Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
contained in the amino acid concentration data of the individual
from which the data such as the defective and outliers have been
removed in the step SA-34 and (ii) the multivariate discriminant
containing at least one or at least two of Glu, Gly, Ser, Ile, Val,
Leu, Phe and Asp as the explanatory variable, and (b) the
calculated discriminant value may be compared with the previously
established threshold (cutoff value), thereby discriminating
between the IGT and the NGT in the individual. Thus, the
discriminant values obtained in the multivariate discriminants
useful for discriminating between the 2 groups of the IGT and the
NGT can be utilized to bring about an effect of enabling an
accurate discrimination between the 2 groups of the IGT and the
NGT.
[0336] In the step SA-35, the multivariate discriminant may be
expressed by one fractional expression or the sum of a plurality of
the fractional expressions, and may contain at least one of Glu,
Ile, Val, Leu, Phe and Asp as the explanatory variable in the
numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Gly and Ser as the
explanatory variable in the denominator in the fractional
expression constituting the multivariate discriminant, or may
contain at least one of Gly and Ser as the explanatory variable in
the numerator in the fractional expression constituting the
multivariate discriminant, and at least one of Glu, Ile, Val, Leu,
Phe and Asp as the explanatory variable in the denominator in the
fractional expression constituting the multivariate discriminant.
Specifically, the multivariate discriminant may be formula 1. Thus,
discriminant values obtained in multivariate discriminants useful
particularly for discriminating between the 2 groups of the IGT and
the NGT can be utilized to bring about an effect of enabling a more
accurate discrimination between the 2 groups of the IGT and the
NGT.
Glu/(His+Cit)+(Phe+Tyr)/Gly (formula 1)
[0337] In the step SA-35, the multivariate discriminant may be any
one of a logistic regression equation, a linear discriminant, a
multiple regression equation, a discriminant prepared by a support
vector machine, a discriminant prepared by a Mahalanobis'
generalized distance method, a discriminant prepared by canonical
discriminant analysis, and a discriminant prepared by a decision
tree. Specifically, the multivariate discriminant may be the
logistic regression equation containing at least Glu and Gly as the
explanatory variables. Thus, discriminant values obtained in
multivariate discriminants useful particularly for discriminating
between the 2 groups of the IGT and the NGT can be utilized to
bring about an effect of enabling a more accurate discrimination
between the 2 groups of the IGT and the NGT.
[0338] The multivariate discriminants described above can be
prepared by a method described in International Publication WO
2004/052191 Pamphlet that is an international application filed by
the present applicant or by a method (multivariate
discriminant-preparing processing described in the second
embodiment described above) described in International Publication
WO 2006/098192 Pamphlet that is an international application filed
by the present applicant. Any multivariate discriminants obtained
by these methods can be preferably used in the evaluation of the
IGT state, regardless of the unit of the amino acid concentration
in the amino acid concentration data as input data.
[0339] In the method of searching for prophylactic/ameliorating
substance for IGT according to the third embodiment, substances
that restore normal values to the concentration values of the amino
acid group containing at least one or at least two of Glu, Gly,
Ser, Ile, Val, Leu, Phe and Asp or the discriminant values of each
of the multivariate discriminants, can be selected by the method of
evaluating IGT in the first embodiment described above or by the
IGT-evaluating apparatus in the second embodiment described
above.
[0340] In the method of searching for prophylactic/ameliorating
substance for IGT in the third embodiment, "searching for
prophylactic/ameliorating substance" includes not only discovery of
a novel substance effective in preventing and ameliorating the IGT,
but also (1) new discovery of use of a known substance in
preventing and ameliorating the IGT, (2) discovery of a novel
composition consisting of a combination of existing drugs and
supplements having efficacy expectable for prevention and
amelioration of the IGT, (3) discovery of the suitable usage, dose
and combination described above to form them into a kit, (4)
presentation of a prophylactic and therapeutic menu including a
diet, exercise etc., and (5) presentation of a necessary change in
menu for each individual by monitoring the effect of the
prophylactic and therapeutic menu.
Example 1
[0341] From among those who received a consultation for OGTT in a
comprehensive medical examination, 304 subjects whose FPG were less
than 110 mg/dl were classified into a NGT (normal glucose
tolerance) group (less than 140 mg/dl, 248 subjects) and an IGT
group (equal to or more than 140 mg/dl, 56 patients) based on the
diagnostic criteria associated with a 2-hour OGTT level. The blood
amino acid concentrations were measured by the amino acid analysis
method described above from each of the blood samples collected
from the subjects. Those who were receiving treatment for disease
such as hypertension and hyperlipidemia were not included in the
304 subjects.
[0342] In FIG. 25, boxplots showing a distribution of the amino
acid explanatory variables between the 2 groups of the NGT and the
IGT are represented. In FIG. 25, the numeral reference "0"
indicated in the horizontal axis of the boxplot represents the NGT
group, while the numeral reference "1" represents the IGT group. In
the figure, the abbreviation "ABA" represents .alpha.-amino butyric
acid, the abbreviation "Cys" represents cystine, and the
abbreviation "Cit" represents citrulline. For the purpose of a
discrimination between the 2 groups, Welch's t-test was carried out
between the 2 groups.
[0343] In the IGT group as compared with the NGT group, Glu, Ile,
Val, Leu, Phe and Asp were significantly increased (significant
difference probability P<0.05), and Gly and Ser were
significantly decreased. It became clear that the amino acid
explanatory variables Glu, Gly, Ser, Ile, Val, Leu, Phe and Asp
have an ability for the discrimination between the 2 groups.
Example 2
[0344] The sample data used in Example 1 were used. An index
formula that would maximize the discrimination capability between
the 2 groups of the NGT and the IGT, was thoroughly searched using
a method described in International Publication WO 2004/052191
Pamphlet. As a result, an index formula
"(Glu)/(His+Cit)+(Phe+Tyr)/(Gly)" was obtained as an example of a
plurality of multivariate discriminants having equivalent
capabilities.
[0345] Diagnostic capability (discrimination capability) of the
index formula between the 2 groups was evaluated by an AUC (area
under the curve) of ROC curve (receiver operating characteristic
curve) (FIG. 26). As a result, an AUC of 0.78 (95% confidence
interval was 0.70 to 0.83) was obtained.
Example 3
[0346] The sample data used in Example 1 were used. An index that
would maximize the discrimination capability between the 2 groups
of the NGT and the IGT was thoroughly searched using a method of
searching a multivariate discriminant described in International
Publication WO 2006/098192 Pamphlet. As the method of searching the
multivariate discriminant, a multivariate discriminant given by a
logistic regression, a linear discrimination, a support vector
machine, Mahalanobis' generalized distance method, and the like can
be used. When multivariate discriminants were thoroughly searched
by using a stepwise logistic analysis as an example, a logistic
regression equation composed of Glu and Gly (numerical coefficients
of amino acid explanatory variables Glu and Gly and constant terms
were, 0.039.+-.0.010, -0.013.+-.0.005, and -0.788.+-.1.187, in
sequence) was obtained as an index formula.
[0347] Diagnostic capability (discrimination capability) of the
index formula between the 2 groups was evaluated by the AUC of the
ROC curve (FIG. 27). As a result, an AUC of 0.75 (95% confidence
interval was 0.69 to 0.82) was obtained, based on which the index
formula was found to be a useful index with high discrimination
capability.
[0348] An optimal cut-off value for discrimination between the 2
groups performed by the index formula was obtained (specifically, a
cut-off value was obtained with respect to an explanatory variable
obtained by subjecting a probability given by a logistic analysis
to a logit transformation). As a result, when prevalence was
provided as 0.18, the cut-off value was -0.64, and sensitivity was
64%, specificity was 71%, a positive predictive value was 33%, a
negative predictive value was 90%, and efficiency was 69%, based on
which the index formula was found to be a useful index with high
discrimination capability.
[0349] As an example other than an index formula obtained by a
logistic analysis, multivariate discriminants given by a linear
discrimination, a support vector machine, and Mahalanobis'
generalized distance method were studied using the same data as
above. Subsequently, diagnostic capability of each discriminant
between the 2 groups was evaluated. As a result, in sequence, an
AUC of ROC curve was 0.74 with respect to an index formula having
Glu, Gly, and Met as explanatory variables in a linear
discrimination; an error rate was 17.8% with respect to an index
formula having Glu, Gln, Gly, and Ala as explanatory variables in a
support vector machine; and an error rate was 18.7% with respect to
an index formula having Asp, Glu, Gly, Cys, Trp, and Arg as
explanatory variables in Mahalanobis' generalized distance method,
based on which each index formula was found to be a useful index
with high discrimination capability similarly to the index formula
obtained by a logistic regression.
Example 4
[0350] The sample data used in Example 1 were used. An index that
would maximize the discrimination capability between the 2 groups
of the NGT and the IGT was thoroughly searched using a method
described in International Publication WO 2004/052191 Pamphlet. As
a result, a plurality of index formulae having an equivalent
capability was obtained. In FIGS. 28 and 29, lists of AUCs of ROC
curves with respect to diagnostic capability (discrimination
capability) of the index formula between the 2 groups are
represented.
Example 5
[0351] The sample data used in Example 1 were used. An index that
would maximize the discrimination capability between the 2 groups
of the NGT and the IGT was searched by a logistic analysis using a
method described in International Publication WO 2006/098192
Pamphlet. As a result, a plurality of index formulae having an
equivalent capability was obtained. In FIGS. 30 and 31, lists of
AUCs of ROC curves with respect to diagnostic capability
(discrimination capability) of the index formula between the 2
groups are represented. In FIGS. 32 and 33, lists of logistic
regression equations including numerical coefficients are
represented.
Example 6
[0352] The sample data used in Example 1 were used. A 2-step
discrimination shown in FIG. 34 was conducted for discrimination
between the 2 groups of the NGT and the IGT. Specifically, in a
first step, "those whose FPG were equal to or more than 100 mg/dl
or those whose hemoglobin A1c (HbA1c) were equal to or more than
5.2%" were discriminated as positive (FPGHb=1) and others were
discriminated as negative (FPGHb=0). Then, in a second step, with
respect to the group of the subjects discriminated as positive
(FPGHb=1) in the first step, an index formula for discrimination
between the 2 groups of the NGT and the IGT was calculated by
stepwise logistic analysis using a method described in
International Publication WO 2006/098192 Pamphlet. As a result, a
logistic regression equation composed of Glu and Gly as an index
formula was obtained. Those who were found to be positive (AI=1) by
the index formula were discriminated to have Impaired glucose
tolerance (IGT/DM). Then, those who were found to be negative
(FPGHb=0) in the first step or negative (AI=0) in the second step
were discriminated as normal glucose tolerance (NGT).
[0353] Regarding discrimination between the 2 groups of the NGT and
the IGT, sensitivity was 75%, specificity was 46%, a positive
predictive value was 24%, a negative predictive value was 89%, and
efficiency was 51% as obtained by the first step alone. However,
once the second step was added, sensitivity was 68%, specificity
was 77%, a positive predictive value was 40%, a negative predictive
value was 91%, and efficiency was 75%. As results, the index
formula obtained in the second step was found to be a useful index
with high discrimination capability.
Example 7
[0354] From among those who received a consultation for OGTT in a
comprehensive medical examination that was conducted at a different
time from Example 1, 90 subjects whose FPG were less than 110 mg/dl
were classified into a NGT group (less than 140 mg/dl, 60 subjects)
and an IGT group (equal to or more than 140 mg/dl, 30 patients)
based on the diagnostic criteria associated with a 2-hour OGTT
level. Those who were receiving treatment for a disease such as
hypertension and hyperlipidemia were not included in the 90
subjects. Then, a blood amino acid concentration was measured in a
sample taken from each subject by an amino acid analysis method
employing a LC-MS method, which was different from the ninhydrin
method described herein. A technical content of the LC-MS method
employed herein was described in International Publication WO
2003/069328 Pamphlet, which was directed to an analytical method
and an analytical reagent for an amino functional compound, and in
International Publication WO 2005/116629 Pamphlet, which was
directed to an analytical method and an apparatus for an amino
functional compound.
[0355] An index that would maximize the discrimination capability
between the 2 groups of the NGT and the IGT was thoroughly searched
using the measured sample data and a method described in
International Publication WO 2004/052191 Pamphlet. As a result, a
plurality of index formulae that were expressed as fractional
expressions and had an equivalent capability were obtained. In
FIGS. 35 and 36, lists of AUCs of ROC curves with respect to
diagnostic capability (discrimination capability) of the index
formulae between the 2 groups are represented.
Example 8
[0356] The sample data used in Example 7 were used. An index that
would maximize the discrimination capability between the 2 groups
of the NGT and the IGT was thoroughly searched using a method
described in International Publication WO 2004/052191 Pamphlet. As
a result, a plurality of index formulae that were expressed as
fractional expressions with real coefficients and had an equivalent
capability were obtained. In FIGS. 37 and 38, lists of AUCs of ROC
curves and the fractional expressions with real coefficients with
respect to diagnostic capability (discrimination capability) of the
index formulae between the 2 groups are represented.
Example 9
[0357] The sample data used in Example 7 were used. An index that
would maximize the discrimination capability between the 2 groups
of the NGT and the IGT was searched by a logistic analysis using a
method described in International Publication WO 2006/098192
Pamphlet. As a result, a plurality of index formulae having an
equivalent capability was obtained. In FIGS. 39 and 40, lists of
AUCs of ROC curves and logistic regression equations including
numerical coefficients with respect to diagnostic capability
(discrimination capability) of the index formulae between the 2
groups are represented.
[0358] Although the invention has been described with respect to
specific embodiments for a complete and clear disclosure, the
appended claims are not to be thus limited but are to be construed
as embodying all modifications and alternative constructions that
may occur to one skilled in the art that fairly fall within the
basic teaching herein set forth.
* * * * *